Determining salient stimuli from the continuously perceived sensory input is a key component for effective learning that has been shaped throughout evolution. The term ‘salient’ is used in psychology and neuroscience to denote a particularly distinct or important stimulus, or an aspect of it (Yantis and Hillstrom, 1994). Attributing salience to a given cue or stimulus, can be affected by genetics, previous experiences, current psychological and motivational state, stress response, goals and motivation (Puglisi-Allegra and Ventura, 2012; Goldberg et al., 2006). Numerous studies have identified cortical and subcortical brain structures that are involved in the detection of salient stimuli, with the insular cortex (IC) being a key node of the salience network (Gogolla, 2017; Livneh et al., 2017; Uddin, 2015). In particular, the IC is believed to have a critical role in the bottom-up detection of salient events and attention allocation (Menon and Uddin, 2010). Thus, when a salient stimulus is detected, the insula assists in targeting other brain structures that enable access to attention and working memory resources.

Salient experiences are comprised of several dimensions, which include novelty amongst others (Modinos et al., 2015; Schultz, 2013; Winton-Brown et al., 2014). Experiencing a new taste therefore represents a salient experience of novel sensory information, and the memory for this event is dependent on the functionality of the IC (Gal-Ben-Ari and Rosenblum, 2011; Yiannakas and Rosenblum, 2017). Taste recognition, upon which novelty and salience detection inversely depend, relies on a combination of instinctive responses (genetic elements) and brain processes involved in recalling past experiences to help animals cautiously avoid poisonous food, while taking advantage of food that is deemed safe. Therefore, when animals encounter a taste that is completely new to them, they approach it with cautiousness, a phenomenon called taste neophobia (Lin et al., 2015). This neophobic response is the first line of defense, and therefore an important and evolutionarily conserved behavior, manifesting in reluctance to consume the novel taste, for fear of potentially adverse outcomes. Following this tentative consumption of the novel taste, and in the absence of any proceeding aversive visceral consequences, a memory for a safe taste is formed (Osorio-Gómez et al., 2018). Thus, the consumption of this taste gradually increases in the following encounters, a phenomenon called attenuation of neophobia (Barot and Bernstein, 2005; Green and Parker, 1975; Morón and Gallo, 2007). This is an example in which a salient, novel stimulus (1) increases attention which affects the preliminary response, and then (2) initiates incidental encoding for a memory of the new experience as to subsequently prime future adaptive behavior (Borsook et al., 2007; Anderson et al., 2006; McGaugh, 2006). Thus, acting when the sensory information is new, and then learning the new stimulus (familiarization), are both crucial but different components in the process of behavior to a salient, novel stimulus.

The process by which a novel taste becomes a familiar one is partially subserved by the IC (Bermudez-Rattoni, 2014), and the gustatory cortex, located within the medial portion of the anterior IC (aIC) (Gehrlach et al., 2020; Shura et al., 2014; Rolls, 2016; Yiannakas and Rosenblum, 2017). The aIC plays a critical role in the formation and retrieval of novel taste memory (Kayyal et al., 2019; Lavi et al., 2018; Yiannakas and Rosenblum, 2017). Inhibition of protein synthesis or lesions to the IC cause impairment in novel taste learning (Rosenblum et al., 1993). Several molecular changes occur in the IC following novel taste learning (Gal-Ben-Ari and Rosenblum, 2011; Gould et al., 2020), including increased phosphorylation-activation of the extracellular regulated kinase (ERK). Phosphorylated ERK (pERK) levels are increased in the IC following novel taste consumption (Berman et al., 1998) and inhibition of MEK, an upstream kinase of ERK in the IC, impairs novel taste learning (Berman et al., 1998).

Although an accumulating body of work has been directed toward understanding the aIC and the molecular mechanisms by which novel taste memory is formed and retrieved (Adaikkan and Rosenblum, 2015; Merhav et al., 2006; Yefet et al., 2006), the cellular and circuit mechanisms are yet to be deciphered.

Additionally to the aIC, the mPFC is another region that plays a prominent component of the novelty network that is engaged following an encounter with a novel taste stimulus (Morici et al., 2015; Takehara-Nishiuchi et al., 2020). Lesions of the mPFC result in impaired learning of novel tasks (Barker et al., 2007; Devito and Eichenbaum, 2011), including taste learning (Gonzalez et al., 2015). The mPFC is reciprocally connected with the IC, however, whether and how their connectivity contributes to novel taste learning has not been comprehensively examined (Gabbott et al., 2003). Hence, we set out to assess the involvement of aIC-mPFC functional connectivity in the neophobic response evoked during novel taste exposure, as well as novel taste learning. To do so, we measured the correlation between the number of activated, pERK⁺ neurons within the general aIC and mPFC populations, and specifically within aIC-to-mPFC or mPFC-to-aIC projecting neurons. We found that experiencing a novel taste causes a significant increase in reciprocal aIC-mPFC neuronal activity as measured by pERK⁺ neurons (Jones et al., 1999). From an electrophysiological perspective, we identify a correlation between taste novelty and increased excitability in aIC-to-mPFC projecting neurons. In order to ascertain behavioral causality of the molecular and electrophysiological correlations we identified, we inhibited the activity of each pathway during the reaction to- or the learning of- a novel taste. We found that chemogenic inhibition of the aIC-to-mPFC pathway resulted both in impaired neophobic response, as well as impaired taste learning and memory. In contrast, inhibition of the opposite, reciprocal, mPFC-to-aIC pathway, caused impairment to the neophobic response only, but not to the learning process.

Our results show that activation of the aIC-mPFC circuit is correlative and necessary for both the neophobic response to novel tastants, as well as memory formation, with functionally discrete reciprocity. This provides part of a functional map of the broader salience system that is yet to be described, as well as providing a guideline for setting the existing molecular knowledge to reveal the unexplored circuit framework.

Detecting salient, new information from the environment is a crucial aspect of human and animal behavior. The detection and proper reaction to a novel stimulus, and the ability to form a stable memory of it, are two distinct facets of novelty behavior. The molecular and cellular mechanisms underlying these centrally important processes, which are critical for animal survival, have been the subject of much research (Mishkin and Murray, 1994; Schomaker and Meeter, 2015). As a result, numerous studies have explored the role of distinct brain areas and molecular or cellular processes within them that are crucial for salience and novelty detection and learning (Peters et al., 2016; van Kesteren et al., 2012). Although studies conducted in humans show a correlation between several brain structures and novelty detection, little is yet known of the detailed novelty network and directionality in the brain, resulting from causative research (Li et al., 2017). It is therefore important to understand how functionally relevant, distal brain regions act as a circuit to define salience and encode unfamiliar sensory information within a wider salience system.

In the present study, we causally demonstrate that reciprocal activity between the aIC and mPFC, or the BLA, conveys novelty related gustatory information, in one such novelty circuit. Specifically, we show that general neuronal activation in the aIC, as denoted by pERK (Thiels and Klann, 2001), is increased following novel taste experience. This is in line with previous studies, showing that ERK phosphorylation in the aIC allows the formation of a memory trace of the safe taste, and is correlated with taste novelty (Sweatt, 2001; Gutkind, 1998; Kelleher et al., 2004; Thomas and Huganir, 2004; Yiannakas and Rosenblum, 2017). Importantly, our results indicate pERK is increased specifically in the AIC and the DIC subregions of the aIC, which have previously been implicated in processing chemosensory, somatosensory visceral and limbic functions (Jones et al., 2006; Katz et al., 2001; Yokota et al., 2011), emphasizing their relevance in novel taste learning.

Extensive reciprocal connections exist between the IC and the mPFC, that are thought to be an essential component in salience processing both in humans and rodents (Uddin, 2015). Here too, our results show a correlation between the number of activated, pERK⁺ neurons within reciprocal aIC-mPFC projections following novel taste experience, suggesting plasticity related changes in aIC-mPFC neurons are initiated following taste learning (Impey et al., 1999; Philips et al., 2013; Purcell et al., 2003; Rosenblum et al., 2002). Interestingly, in the aIC this was particularly apparent in the AIC, while in the mPFC the spread of pERK⁺ neurons was more generalized, being detected across the PrL, InfrL and Cg. The projections of the aIC to the mPFC mainly originate from the AIC (compared to the DIC or GIC), which could be a possible explanation for the specific subregion-effect.

Recent reports indicate the capability of PrL neurons to form ensemble codes for novel stimulus associations within minutes (Takehara-Nishiuchi et al., 2020). This ability seems to underlie the necessity of the mPFC to rapidly detect and selectively encode novel experiences, although our data suggest a role for all mPFC subregions and layers in novel taste processing and learning. Whether and how mPFC subregions, as well as the emerging local circuitry of the aIC are involved in novel taste learning via cell- and subregion-specific mechanisms, on a molecular and cellular level across different phases of learning, will be the subject of future research.

We found that ERK activation within aIC-to-PFC projections correlated with taste novelty, and given ERK involvement in maintaining long-term memory-relevant excitability changes (Cohen-Matsliah et al., 2007), we assessed the intrinsic properties of these projections subsequent to novel taste consumption. In line with the pERK results, we found that aIC-to-mPFC neurons display distinct electrophysiological properties following novel taste consumption. This is in agreement with previous studies in other modalities, showing that novel stimuli and learning of associative experience differentially affect intrinsic properties of neurons in cortical and subcortical regions (Kaczorowski et al., 2012; Sehgal et al., 2013; Sehgal et al., 2014; Song et al., 2015; Song et al., 2015). The increased excitability of aIC-to-mPFC neurons following novel taste experience was observed only in the deep layer of the aIC. Limbic and gustatory information converge in the deep layers of the entire IC, and the main output source of the IC to other cortical and subcortical regions lies within these layers (Kobayashi, 2011; Krushel and van Der Kooy, 1988). In addition, the percentage of taste-coding neurons is higher in the deep layers of the aIC in comparison with the superficial layers (Dikecligil et al., 2020). Our results further strengthen the involvement of the deep layers of the aIC in processing and transmitting taste-related information to other cortical regions, suggesting that these specific molecular and intrinsic changes of aIC-to-mPFC neurons of the deep layers of the aIC allow the formation of a familiar and safe taste memory trace.

Importantly, even though the correlated increase in pERK and in excitability of aIC-mPFC projections following a novel taste demonstrate the involvement of this circuit in novel taste processing, it does not differentiate between the two components of novel taste behavior: novel taste response, or novel taste learning. We thus evaluated if indeed there is a functional aIC-mPFC circuit, by using chemogenetic inhibition to causally examine the reciprocal role of both components, the aIC and the mPFC, in novel taste expression or learning. We found that inhibition of aIC-to-mPFC projecting neurons during novel taste exposure resulted in impaired neophobic responses and novel taste learning. In contrast, inhibiting mPFC-to-aIC projections impaired the expression of neophobia, but not taste memory formation.

This indicates that the mPFC plays a major role in novelty gating, while the detection of novelty and storage of familiar taste memories are both mediated by the aIC. We show that aIC-to-mPFC projections need to be activated during novel taste exposure in order to form a safe taste memory trace, while aIC activation from the mPFC is necessary in order to evoke appropriate behavioral responses to novelty. We thus causally demonstrate that specific connectivity within the aIC and the mPFC allows the identification, the learning and reaction to novel taste stimuli with the memory being formed in the aIC. However, this does not exclude the involvement of other additional stimuli or other circuits that were not the focus of the current study.

The entire IC is an integrative hub for saliency processing. We recently demonstrated that the reciprocal connectivity with the BLA underlies aversive valence processing (Kayyal et al., 2019; Lavi et al., 2018). The BLA is an essential structure for encoding salient stimuli (Fontanini and Katz, 2009) and reward learning (Fontanini et al., 2009), and its signals to the aIC transmit palatability related information (Piette et al., 2012), while facilitating the acquisition and recall of sensory information related to body states, as well as the processing of valence (Gogolla, 2017; Haley and Maffei, 2018; Tye, 2018). Although we have previously shown that activity within aIC-to-BLA projecting neurons is not necessary for novel taste learning, we did not test if it is necessary for the expression of taste neophobia. Here, we show that activity within aIC-to-BLA projecting neurons is essential for the expression of neophobic responses. This goes along with previous data showing that inactivation of the BLA impairs the neophobic response but does not affect the process of attenuation of neophobia, and therefore learning (Lin et al., 2018; Shinohara and Yasoshima, 2019).

Together, our findings suggest that aIC connectivity to different brain areas differentially conveys the novelty state (aIC-to-mPFC/BLA), while following learning-induced plasticity, a now modified circuit state underlies the encoding of the familiarized taste (aIC-to-mPFC but not to BLA) (Figure 7). A functional reciprocal connectivity exists between the BLA and the mPFC (Yizhar and Klavir, 2018), that is of great importance in relation to drinking behavior and water intake (Zhao et al., 2020). In addition, activity within BLA-mPFC projections is necessary for valence-specific behaviors (Yizhar and Klavir, 2018) including innate fear responses (Jhang et al., 2018). Hence, our results do not exclude the possibility of mPFC-BLA activity during novel taste experience and/or learning, but rather suggest a triage of IC, BLA and mPFC participation in novel taste behavior.

Figure 7

Download asset Open asset

Detecting, responding and remembering salient information is a vital property of animal behavior. We show here, for the first time, that the interplay between the aIC and the mPFC in the mammalian brain grasps within it novelty related data, as part of a salience network. Both aIC-mPFC and aIC-to-BLA reciprocal connections are necessary for exhibiting a neophobic response; however, the aIC-mPFC is necessary also to form a memory for a familiar safe taste. In addition, the acquisition and retrieval of learned associations between novel taste and aversive visceral information requires the activity of aIC-to-BLA projecting neurons (Kayyal et al., 2019; Lavi et al., 2018). Together, it places the insula as an integrator of two facets of taste saliency; the novel/familiar axis and aversive/appetitive axis. In addition to the necessity of aIC-mPFC interaction in taste novelty behavior and learning, a recent study reported a prominent role of mPFC-to-AIC in learning of novel olfactory tasks (Zhu et al., 2020). However, it is yet to be defined if and how the IC-mPFC circuitry encodes saliency for other modalities.

There is a growing body of evidence showing that impairment in entire IC-mPFC circuitry is found in schizophrenia, addiction, and depression (Cisler et al., 2013; Penner et al., 2016; Samara et al., 2018), raising the importance for further investigation of this circuit and its dysfunction in patients. Defining the molecular and cellular mechanisms by which salience of sensory or innate information is processed and defined remains a key question in basic and clinical neuroscience. Our results suggest that while the mPFC plays a critical role in reacting to a novel cue, the aIC is essential both in reacting and in learning of the novel stimulus. This suggests that proposed non-invasive treatments for schizophrenia or addiction (such as transcranial magnetic stimulation therapy) could benefit from regiments that target the IC and/or other salience network components, while the integrity of these networks could be of particular pathognomonic value in the developing and adult brain.

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for all figures.

1. 1. Adaikkan C
   2. Rosenblum K

   (2012) Th e role of protein phosphorylation in the gustatory cortex and amygdala during taste learning

   Experimental Neurobiology 21:37–51.

   https://doi.org/10.5607/en.2012.21.2.37

   • Google Scholar
2. 1. Adaikkan C
   2. Rosenblum K

   (2015) A molecular mechanism underlying gustatory memory trace for an association in the insular cortex

   eLife 4:e07582.

   https://doi.org/10.7554/eLife.07582

   • PubMed
   • Google Scholar
3. 1. Anderson MI
   2. Killing S
   3. Morris C
   4. O'Donoghue A
   5. Onyiagha D
   6. Stevenson R
   7. Verriotis M
   8. Jeffery KJ

   (2006) Behavioral correlates of the distributed coding of spatial context

   Hippocampus 16:730–742.

   https://doi.org/10.1002/hipo.20206

   • PubMed
   • Google Scholar
4. 1. Barker GR
   2. Bird F
   3. Alexander V
   4. Warburton EC

   (2007) Recognition memory for objects, place, and temporal order: a disconnection analysis of the role of the medial prefrontal cortex and perirhinal cortex

   Journal of Neuroscience 27:2948–2957.

   https://doi.org/10.1523/JNEUROSCI.5289-06.2007

   • PubMed
   • Google Scholar
5. 1. Barot SK
   2. Bernstein IL

   (2005) Polycose taste pre-exposure fails to influence behavioral and neural indices of taste novelty

   Behavioral Neuroscience 119:1640–1647.

   https://doi.org/10.1037/0735-7044.119.6.1640

   • PubMed
   • Google Scholar
6. 1. Berman DE
   2. Hazvi S
   3. Rosenblum K
   4. Seger R
   5. Dudai Y

   (1998) Specific and differential activation of mitogen-activated protein kinase cascades by unfamiliar taste in the insular cortex of the behaving rat

   The Journal of Neuroscience 18:10037–10044.

   https://doi.org/10.1523/JNEUROSCI.18-23-10037.1998

   • PubMed
   • Google Scholar
7. 1. Bermudez-Rattoni F

   (2014) The forgotten insular cortex: its role on recognition memory formation

   Neurobiology of Learning and Memory 109:207–216.

   https://doi.org/10.1016/j.nlm.2014.01.001

   • PubMed
   • Google Scholar
8. 1. Borsook D
   2. Becerra L
   3. Carlezon WA
   4. Shaw M
   5. Renshaw P
   6. Elman I
   7. Levine J

   (2007) Reward-aversion circuitry in analgesia and pain: implications for psychiatric disorders

   European Journal of Pain 11:7.

   https://doi.org/10.1016/j.ejpain.2005.12.005

   • PubMed
   • Google Scholar
9. 1. Cisler JM
   2. Elton A
   3. Kennedy AP
   4. Young J
   5. Smitherman S
   6. Andrew James G
   7. Kilts CD

   (2013) Altered functional connectivity of the insular cortex across prefrontal networks in cocaine addiction

   Psychiatry Research: Neuroimaging 213:39–46.

   https://doi.org/10.1016/j.pscychresns.2013.02.007

   • PubMed
   • Google Scholar
10. 1. Cohen-Matsliah SI
    2. Brosh I
    3. Rosenblum K
    4. Barkai E

    (2007) A novel role for extracellular signal-regulated kinase in maintaining long-term memory-relevant excitability changes

    Journal of Neuroscience 27:12584–12589.

    https://doi.org/10.1523/JNEUROSCI.3728-07.2007

    • PubMed
    • Google Scholar
11. 1. Devito LM
    2. Eichenbaum H

    (2011) Memory for the order of events in specific sequences: contributions of the Hippocampus and medial prefrontal cortex

    Journal of Neuroscience 31:3169–3175.

    https://doi.org/10.1523/JNEUROSCI.4202-10.2011

    • PubMed
    • Google Scholar
12. 1. Dikecligil GN
    2. Graham DM
    3. Park IM
    4. Fontanini A

    (2020) Layer- and cell Type-Specific response properties of gustatory cortex neurons in awake mice

    The Journal of Neuroscience 40:9676–9691.

    https://doi.org/10.1523/JNEUROSCI.1579-19.2020

    • PubMed
    • Google Scholar
13. 1. Elkobi A
    2. Ehrlich I
    3. Belelovsky K
    4. Barki-Harrington L
    5. Rosenblum K

    (2008) ERK-dependent PSD-95 induction in the gustatory cortex is necessary for taste learning, but not retrieval

    Nature Neuroscience 11:1149–1151.

    https://doi.org/10.1038/nn.2190

    • PubMed
    • Google Scholar
14. 1. Euston DR
    2. Gruber AJ
    3. McNaughton BL

    (2012) The role of medial prefrontal cortex in memory and decision making

    Neuron 76:1057–1070.

    https://doi.org/10.1016/j.neuron.2012.12.002

    • PubMed
    • Google Scholar
15. 1. Fontanini A
    2. Grossman SE
    3. Figueroa JA
    4. Katz DB

    (2009) Distinct subtypes of basolateral amygdala taste neurons reflect palatability and reward

    Journal of Neuroscience 29:2486–2495.

    https://doi.org/10.1523/JNEUROSCI.3898-08.2009

    • PubMed
    • Google Scholar
16. 1. Fontanini A
    2. Katz DB

    (2009) Behavioral modulation of gustatory cortical activity

    Annals of the New York Academy of Sciences 1170:403–406.

    https://doi.org/10.1111/j.1749-6632.2009.03922.x

    • PubMed
    • Google Scholar
17. 1. Gabbott PL
    2. Warner TA
    3. Jays PR
    4. Bacon SJ

    (2003) Areal and synaptic interconnectivity of prelimbic (area 32), infralimbic (area 25) and insular cortices in the rat

    Brain Research 993:59–71.

    https://doi.org/10.1016/j.brainres.2003.08.056

    • PubMed
    • Google Scholar
18. 1. Gal-Ben-Ari S
    2. Rosenblum K

    (2011) Molecular mechanisms underlying memory consolidation of taste information in the cortex

    Frontiers in Behavioral Neuroscience 5:87.

    https://doi.org/10.3389/fnbeh.2011.00087

    • PubMed
    • Google Scholar
19. 1. Gehrlach DA
    2. Weiand C
    3. Gaitanos TN
    4. Cho E
    5. Klein AS
    6. Hennrich AA
    7. Conzelmann KK
    8. Gogolla N

    (2020) A whole-brain connectivity map of mouse insular cortex

    eLife 9:e55585.

    https://doi.org/10.7554/eLife.55585

    • PubMed
    • Google Scholar
20. 1. Gogolla N

    (2017) The insular cortex

    Current Biology 27:R580–R586.

    https://doi.org/10.1016/j.cub.2017.05.010

    • PubMed
    • Google Scholar
21. 1. Goldberg ME
    2. Bisley JW
    3. Powell KD
    4. Gottlieb J

    (2006) Saccades, salience and attention: the role of the lateral intraparietal area in visual behavior

    Progress in Brain Research 155:157–175.

    https://doi.org/10.1016/S0079-6123(06)55010-1

    • PubMed
    • Google Scholar
22. 1. Gonzalez MC
    2. Villar ME
    3. Igaz LM
    4. Viola H
    5. Medina JH

    (2015) Dorsal medial prefrontal cortex contributes to conditioned taste aversion memory consolidation and retrieval

    Neurobiology of Learning and Memory 126:1–6.

    https://doi.org/10.1016/j.nlm.2015.10.007

    • PubMed
    • Google Scholar
23. 1. Gould NL
    2. Elkobi A
    3. Edry E
    4. Daume J
    5. Rosenblum K

    (2020) Muscarinic-Dependent miR-182 and QR2 expression regulation in the anterior insula enables novel taste learning

    Eneuro 7:ENEURO.0067-20.2020.

    https://doi.org/10.1523/ENEURO.0067-20.2020

    • PubMed
    • Google Scholar
24. 1. Green KF
    2. Parker LA

    (1975) Gustatory memory: incubation and interference

    Behavioral Biology 13:359–367.

    https://doi.org/10.1016/S0091-6773(75)91416-9

    • PubMed
    • Google Scholar
25. 1. Gulledge AT
    2. Dasari S
    3. Onoue K
    4. Stephens EK
    5. Hasse JM
    6. Avesar D

    (2013) A sodium-pump-mediated afterhyperpolarization in pyramidal neurons

    Journal of Neuroscience 33:13025–13041.

    https://doi.org/10.1523/JNEUROSCI.0220-13.2013

    • PubMed
    • Google Scholar
26. 1. Gutkind JS

    (1998) The pathways connecting G Protein-coupled receptors to the nucleus through divergent Mitogen-activated protein kinase cascades

    Journal of Biological Chemistry 273:1839–1842.

    https://doi.org/10.1074/jbc.273.4.1839

    • Google Scholar
27. 1. Haley MS
    2. Maffei A

    (2018) Versatility and flexibility of cortical circuits

    The Neuroscientist 24:456–470.

    https://doi.org/10.1177/1073858417733720

    • Google Scholar
28. 1. Harris KD
    2. Mrsic-Flogel TD

    (2013) Cortical connectivity and sensory coding

    Nature 503:51–58.

    https://doi.org/10.1038/nature12654

    • PubMed
    • Google Scholar
29. 1. Impey S
    2. Obrietan K
    3. Storm DR

    (1999) Making new connections: role of ERK/MAP kinase signaling in neuronal plasticity

    Neuron 23:11–14.

    https://doi.org/10.1016/s0896-6273(00)80747-3

    • PubMed
    • Google Scholar
30. 1. Jhang J
    2. Lee H
    3. Kang MS
    4. Lee HS
    5. Park H
    6. Han JH

    (2018) Anterior cingulate cortex and its input to the basolateral amygdala control innate fear response

    Nature Communications 9:90.

    https://doi.org/10.1038/s41467-018-05090-y

    • PubMed
    • Google Scholar
31. 1. Jones MW
    2. French PJ
    3. Bliss TV
    4. Rosenblum K

    (1999) Molecular mechanisms of long-term potentiation in the insular cortex in vivo

    The Journal of Neuroscience 19:RC36.

    https://doi.org/10.1523/JNEUROSCI.19-21-j0002.1999

    • PubMed
    • Google Scholar
32. 1. Jones LM
    2. Fontanini A
    3. Katz DB

    (2006) Gustatory processing: a dynamic systems approach

    Current Opinion in Neurobiology 16:420–428.

    https://doi.org/10.1016/j.conb.2006.06.011

    • PubMed
    • Google Scholar
33. 1. Kaczorowski CC
    2. Davis SJ
    3. Moyer JR

    (2012) Aging redistributes medial prefrontal neuronal excitability and impedes extinction of trace fear conditioning

    Neurobiology of Aging 33:1744–1757.

    https://doi.org/10.1016/j.neurobiolaging.2011.03.020

    • PubMed
    • Google Scholar
34. 1. Kaphzan H
    2. Buffington SA
    3. Ramaraj AB
    4. Lingrel JB
    5. Rasband MN
    6. Santini E
    7. Klann E

    (2013) Genetic reduction of the α1 subunit of na/K-ATPase corrects multiple hippocampal phenotypes in angelman syndrome

    Cell Reports 4:405–412.

    https://doi.org/10.1016/j.celrep.2013.07.005

    • PubMed
    • Google Scholar
35. 1. Katz DB
    2. Simon SA
    3. Nicolelis MA

    (2001) Dynamic and multimodal responses of gustatory cortical neurons in awake rats

    The Journal of Neuroscience 21:4478–4489.

    https://doi.org/10.1523/JNEUROSCI.21-12-04478.2001

    • PubMed
    • Google Scholar
36. 1. Kayyal H
    2. Yiannakas A
    3. Kolatt Chandran S
    4. Khamaisy M
    5. Sharma V
    6. Rosenblum K

    (2019) Activity of insula to basolateral amygdala projecting neurons is necessary and sufficient for taste valence representation

    The Journal of Neuroscience 39:9369–9382.

    https://doi.org/10.1523/JNEUROSCI.0752-19.2019

    • PubMed
    • Google Scholar
37. 1. Kelleher RJ
    2. Govindarajan A
    3. Jung HY
    4. Kang H
    5. Tonegawa S

    (2004) Translational control by MAPK signaling in long-term synaptic plasticity and memory

    Cell 116:467–479.

    https://doi.org/10.1016/S0092-8674(04)00115-1

    • PubMed
    • Google Scholar
38. 1. Kobayashi M

    (2011) Macroscopic connection of rat insular cortex: anatomical bases underlying its physiological functions

    International Review of Neurobiology 97:285–303.

    https://doi.org/10.1016/B978-0-12-385198-7.00011-4

    • PubMed
    • Google Scholar
39. 1. Krushel LA
    2. van Der Kooy D

    (1988) Visceral cortex: integration of the mucosal senses with limbic information in the rat agranular insular cortex

    The Journal of Comparative Neurology 270:39–54.

    https://doi.org/10.1002/cne.902700105

    • Google Scholar
40. 1. Lavi K
    2. Jacobson GA
    3. Rosenblum K
    4. Lüthi A

    (2018) Encoding of conditioned taste aversion in Cortico-Amygdala circuits

    Cell Reports 24:278–283.

    https://doi.org/10.1016/j.celrep.2018.06.053

    • PubMed
    • Google Scholar
41. 1. Li S
    2. Demenescu LR
    3. Sweeney-Reed CM
    4. Krause AL
    5. Metzger CD
    6. Walter M

    (2017) Novelty seeking and reward dependence-related large-scale brain networks functional connectivity variation during salience expectancy

    Human Brain Mapping 38:4064–4077.

    https://doi.org/10.1002/hbm.23648

    • PubMed
    • Google Scholar
42. 1. Lin JY
    2. Arthurs J
    3. Reilly S

    (2015) Gustatory insular cortex, aversive taste memory and taste neophobia

    Neurobiology of Learning and Memory 119:77–84.

    https://doi.org/10.1016/j.nlm.2015.01.005

    • PubMed
    • Google Scholar
43. 1. Lin JY
    2. Arthurs J
    3. Reilly S

    (2018) The effects of amygdala and cortical inactivation on taste neophobia

    Neurobiology of Learning and Memory 155:322–329.

    https://doi.org/10.1016/j.nlm.2018.08.021

    • PubMed
    • Google Scholar
44. 1. Livneh Y
    2. Ramesh RN
    3. Burgess CR
    4. Levandowski KM
    5. Madara JC
    6. Fenselau H
    7. Goldey GJ
    8. Diaz VE
    9. Jikomes N
    10. Resch JM
    11. Lowell BB
    12. Andermann ML

    (2017) Homeostatic circuits selectively gate food cue responses in insular cortex

    Nature 546:611–616.

    https://doi.org/10.1038/nature22375

    • PubMed
    • Google Scholar
45. 1. McGaugh JL

    (2006) Make mild moments memorable: add a little arousal

    Trends in Cognitive Sciences 10:345–347.

    https://doi.org/10.1016/j.tics.2006.06.001

    • PubMed
    • Google Scholar
46. 1. Menon V
    2. Uddin LQ

    (2010) Saliency, switching, attention and control: a network model of insula function

    Brain Structure and Function 214:655–667.

    https://doi.org/10.1007/s00429-010-0262-0

    • PubMed
    • Google Scholar
47. 1. Merhav M
    2. Kuulmann-Vander S
    3. Elkobi A
    4. Jacobson-Pick S
    5. Karni A
    6. Rosenblum K

    (2006) Behavioral interference and C/EBPbeta expression in the insular-cortex reveal a prolonged time period for taste memory consolidation

    Learning & Memory 13:571–574.

    https://doi.org/10.1101/lm.282406

    • PubMed
    • Google Scholar
48. 1. Mickley GA
    2. Hoxha Z
    3. Bacik S
    4. Kenmuir CL
    5. Wellman JA
    6. Biada JM
    7. DiSorbo A

    (2007) Spontaneous recovery of a conditioned taste aversion differentially alters extinction-induced changes in c-Fos protein expression in rat amygdala and neocortex

    Brain Research 1152:139–157.

    https://doi.org/10.1016/j.brainres.2007.03.050

    • PubMed
    • Google Scholar
49. 1. Mishkin M
    2. Murray EA

    (1994) Stimulus recognition

    Current Opinion in Neurobiology 4:200–206.

    https://doi.org/10.1016/0959-4388(94)90073-6

    • PubMed
    • Google Scholar
50. 1. Modinos G
    2. Allen P
    3. Grace AA
    4. McGuire P

    (2015) Translating the MAM model of psychosis to humans

    Trends in Neurosciences 38:129–138.

    https://doi.org/10.1016/j.tins.2014.12.005

    • Google Scholar
51. 1. Morici JF
    2. Bekinschtein P
    3. Weisstaub NV

    (2015) Medial prefrontal cortex role in recognition memory in rodents

    Behavioural Brain Research 292:241–251.

    https://doi.org/10.1016/j.bbr.2015.06.030

    • PubMed
    • Google Scholar
52. 1. Morón I
    2. Gallo M

    (2007) Effect of previous taste experiences on taste neophobia in young-adult and aged rats

    Physiology & Behavior 90:308–317.

    https://doi.org/10.1016/j.physbeh.2006.09.036

    • PubMed
    • Google Scholar
53. Book

    1. Osorio-Gómez D
    2. Guzmán-Ramos K
    3. Bermúdez-Rattoni F

    (2018) Neurobiology of neophobia and its attenuation Food Neophobia

    In: Osorio-Gómez D, editors. Behavioral and Biological Influences. Elsevier. pp. 111–128.

    https://doi.org/10.1016/B978-0-08-101931-3.00006-9

    • Google Scholar
54. 1. Penner J
    2. Ford KA
    3. Taylor R
    4. Schaefer B
    5. Théberge J
    6. Neufeld RW
    7. Osuch EA
    8. Menon RS
    9. Rajakumar N
    10. Allman JM
    11. Williamson PC

    (2016) Medial prefrontal and anterior insular connectivity in early schizophrenia and major depressive disorder: a resting functional MRI evaluation of Large-Scale brain network models

    Frontiers in Human Neuroscience 10:132.

    https://doi.org/10.3389/fnhum.2016.00132

    • PubMed
    • Google Scholar
55. 1. Peters SK
    2. Dunlop K
    3. Downar J

    (2016) Cortico-Striatal-Thalamic loop circuits of the salience network: a central pathway in psychiatric disease and treatment

    Frontiers in Systems Neuroscience 10:104.

    https://doi.org/10.3389/fnsys.2016.00104

    • PubMed
    • Google Scholar
56. 1. Philips GT
    2. Ye X
    3. Kopec AM
    4. Carew TJ

    (2013) MAPK establishes a molecular context that defines effective training patterns for long-term memory formation

    Journal of Neuroscience 33:7565–7573.

    https://doi.org/10.1523/JNEUROSCI.5561-12.2013

    • PubMed
    • Google Scholar
57. 1. Piette CE
    2. Baez-Santiago MA
    3. Reid EE
    4. Katz DB
    5. Moran A

    (2012) Inactivation of basolateral amygdala specifically eliminates palatability-related information in cortical sensory responses

    Journal of Neuroscience 32:9981–9991.

    https://doi.org/10.1523/JNEUROSCI.0669-12.2012

    • PubMed
    • Google Scholar
58. 1. Puglisi-Allegra S
    2. Ventura R

    (2012) Prefrontal/accumbal catecholamine system processes emotionally driven attribution of motivational salience

    Reviews in the Neurosciences 23:509–526.

    https://doi.org/10.1515/revneuro-2012-0076

    • PubMed
    • Google Scholar
59. 1. Purcell AL
    2. Sharma SK
    3. Bagnall MW
    4. Sutton MA
    5. Carew TJ

    (2003) Activation of a tyrosine Kinase-MAPK cascade enhances the induction of Long-Term synaptic facilitation and Long-Term memory in Aplysia

    Neuron 37:473–484.

    https://doi.org/10.1016/S0896-6273(03)00030-8

    • Google Scholar
60. 1. Rolls ET

    (2016) Functions of the anterior insula in taste, autonomic, and related functions

    Brain and Cognition 110:4–19.

    https://doi.org/10.1016/j.bandc.2015.07.002

    • PubMed
    • Google Scholar
61. 1. Rosenblum K
    2. Meiri N
    3. Dudai Y

    (1993) Taste memory: the role of protein synthesis in gustatory cortex

    Behavioral and Neural Biology 59:49–56.

    https://doi.org/10.1016/0163-1047(93)91145-D

    • PubMed
    • Google Scholar
62. 1. Rosenblum K
    2. Futter M
    3. Voss K
    4. Erent M
    5. Skehel PA
    6. French P
    7. Obosi L
    8. Jones MW
    9. Bliss TV

    (2002) The role of extracellular regulated kinases I/II in late-phase long-term potentiation

    The Journal of Neuroscience 22:5432–5441.

    https://doi.org/10.1523/JNEUROSCI.22-13-05432.2002

    • PubMed
    • Google Scholar
63. 1. Samara Z
    2. Evers EAT
    3. Peeters F
    4. Uylings HBM
    5. Rajkowska G
    6. Ramaekers JG
    7. Stiers P

    (2018) Orbital and medial prefrontal cortex functional connectivity of major depression vulnerability and disease

    Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 3:348–357.

    https://doi.org/10.1016/j.bpsc.2018.01.004

    • Google Scholar
64. 1. Schomaker J
    2. Meeter M

    (2015) Short- and long-lasting consequences of novelty, deviance and surprise on brain and cognition

    Neuroscience & Biobehavioral Reviews 55:268–279.

    https://doi.org/10.1016/j.neubiorev.2015.05.002

    • Google Scholar
65. 1. Schultz W

    (2013) Updating dopamine reward signals

    Current Opinion in Neurobiology 23:229–238.

    https://doi.org/10.1016/j.conb.2012.11.012

    • PubMed
    • Google Scholar
66. 1. Sehgal M
    2. Song C
    3. Ehlers VL
    4. Moyer JR

    (2013) Learning to learn - intrinsic plasticity as a metaplasticity mechanism for memory formation

    Neurobiology of Learning and Memory 105:186–199.

    https://doi.org/10.1016/j.nlm.2013.07.008

    • PubMed
    • Google Scholar
67. 1. Sehgal M
    2. Ehlers VL
    3. Moyer JR

    (2014) Learning enhances intrinsic excitability in a subset of lateral amygdala neurons

    Learning & Memory 21:161–170.

    https://doi.org/10.1101/lm.032730.113

    • Google Scholar
68. 1. Seibenhener ML
    2. Wooten MC

    (2015) Use of the open field maze to measure locomotor and Anxiety-like behavior in mice

    Journal of Visualized Experiments 96:e52434.

    https://doi.org/10.3791/52434

    • Google Scholar
69. 1. Sharma V
    2. Ounallah-Saad H
    3. Chakraborty D
    4. Hleihil M
    5. Sood R
    6. Barrera I
    7. Edry E
    8. Kolatt Chandran S
    9. Ben Tabou de Leon S
    10. Kaphzan H
    11. Rosenblum K

    (2018) Local inhibition of PERK enhances memory and reverses Age-Related deterioration of cognitive and neuronal properties

    The Journal of Neuroscience 38:648–658.

    https://doi.org/10.1523/JNEUROSCI.0628-17.2017

    • PubMed
    • Google Scholar
70. 1. Shinohara K
    2. Yasoshima Y

    (2019) Inactivation of the basolateral amygdala suppresses the expression of taste neophobia but not the retrieval process in attenuation of neophobia

    Behavioural Brain Research 372:112010.

    https://doi.org/10.1016/j.bbr.2019.112010

    • PubMed
    • Google Scholar
71. 1. Shura RD
    2. Hurley RA
    3. Taber KH

    (2014) Insular cortex: structural and functional neuroanatomy

    The Journal of Neuropsychiatry and Clinical Neurosciences 26:iv–282.

    https://doi.org/10.1176/appi.neuropsych.260401

    • Google Scholar
72. 1. Song C
    2. Ehlers VL
    3. Moyer JR

    (2015) Trace fear conditioning differentially modulates intrinsic excitability of medial prefrontal Cortex-Basolateral complex of amygdala projection neurons in Infralimbic and prelimbic cortices

    Journal of Neuroscience 35:13511–13524.

    https://doi.org/10.1523/JNEUROSCI.2329-15.2015

    • PubMed
    • Google Scholar
73. 1. Sweatt JD

    (2001) The neuronal MAP kinase cascade: a biochemical signal integration system subserving synaptic plasticity and memory

    Journal of Neurochemistry 76:1–10.

    https://doi.org/10.1046/j.1471-4159.2001.00054.x

    • Google Scholar
74. 1. Takehara-Nishiuchi K
    2. Morrissey MD
    3. Pilkiw M

    (2020) Prefrontal neural ensembles develop selective code for stimulus associations within minutes of novel experiences

    The Journal of Neuroscience 40:8355–8366.

    https://doi.org/10.1523/JNEUROSCI.1503-20.2020

    • PubMed
    • Google Scholar
75. 1. Thiels E
    2. Klann E

    (2001) Extracellular Signal-Regulated kinase, synaptic plasticity, and memory

    Reviews in the Neurosciences 12:327–345.

    https://doi.org/10.1515/REVNEURO.2001.12.4.327

    • Google Scholar
76. 1. Thomas GM
    2. Huganir RL

    (2004) MAPK cascade signalling and synaptic plasticity

    Nature Reviews Neuroscience 5:173–183.

    https://doi.org/10.1038/nrn1346

    • Google Scholar
77. 1. Tye KM

    (2018) Neural circuit motifs in Valence processing

    Neuron 100:436–452.

    https://doi.org/10.1016/j.neuron.2018.10.001

    • Google Scholar
78. 1. Uddin LQ

    (2015) Salience processing and insular cortical function and dysfunction

    Nature Reviews Neuroscience 16:55–61.

    https://doi.org/10.1038/nrn3857

    • Google Scholar
79. 1. Uematsu A
    2. Kitamura A
    3. Iwatsuki K
    4. Uneyama H
    5. Tsurugizawa T

    (2015) Correlation between activation of the prelimbic cortex, Basolateral Amygdala, and agranular insular cortex during taste memory formation

    Cerebral Cortex 25:2719–2728.

    https://doi.org/10.1093/cercor/bhu069

    • PubMed
    • Google Scholar
80. 1. van Kesteren MTR
    2. Ruiter DJ
    3. Fernández G
    4. Henson RN

    (2012) How schema and novelty augment memory formation

    Trends in Neurosciences 35:211–219.

    https://doi.org/10.1016/j.tins.2012.02.001

    • Google Scholar
81. 1. Winton-Brown TT
    2. Fusar-Poli P
    3. Ungless MA
    4. Howes OD

    (2014) Dopaminergic basis of salience dysregulation in psychosis

    Trends in Neurosciences 37:85–94.

    https://doi.org/10.1016/j.tins.2013.11.003

    • Google Scholar
82. 1. Yantis S
    2. Hillstrom AP

    (1994) Stimulus-driven attentional capture: evidence from equiluminant visual objects

    Journal of Experimental Psychology: Human Perception and Performance 20:95–107.

    https://doi.org/10.1037/0096-1523.20.1.95

    • Google Scholar
83. 1. Yefet K
    2. Merhav M
    3. Kuulmann-Vander S
    4. Elkobi A
    5. Belelovsky K
    6. Jacobson-Pick S
    7. Meiri N
    8. Rosenblum K

    (2006) Different signal transduction cascades are activated simultaneously in the rat insular cortex and Hippocampus following novel taste learning

    European Journal of Neuroscience 24:1434–1442.

    https://doi.org/10.1111/j.1460-9568.2006.05009.x

    • Google Scholar
84. 1. Yiannakas A
    2. Rosenblum K

    (2017) The insula and taste learning

    Frontiers in Molecular Neuroscience 10:335.

    https://doi.org/10.3389/fnmol.2017.00335

    • PubMed
    • Google Scholar
85. 1. Yizhar O
    2. Klavir O

    (2018) Reciprocal amygdala-prefrontal interactions in learning

    Current Opinion in Neurobiology 52:149–155.

    https://doi.org/10.1016/j.conb.2018.06.006

    • PubMed
    • Google Scholar
86. 1. Yokota T
    2. Eguchi K
    3. Hiraba K

    (2011) Functional properties of putative pyramidal neurons and inhibitory interneurons in the rat gustatory cortex

    Cerebral Cortex 21:597–606.

    https://doi.org/10.1093/cercor/bhq126

    • PubMed
    • Google Scholar
87. 1. Zhao Z
    2. Soria-Gómez E
    3. Varilh M
    4. Covelo A
    5. Julio-Kalajzić F
    6. Cannich A
    7. Castiglione A
    8. Vanhoutte L
    9. Duveau A
    10. Zizzari P
    11. Beyeler A
    12. Cota D
    13. Bellocchio L
    14. Busquets-Garcia A
    15. Marsicano G

    (2020) A novel cortical mechanism for Top-Down control of water intake

    Current Biology 30:4789–4798.

    https://doi.org/10.1016/j.cub.2020.09.011

    • PubMed
    • Google Scholar
88. 1. Zhu J
    2. Cheng Q
    3. Chen Y
    4. Fan H
    5. Han Z
    6. Hou R
    7. Chen Z
    8. Li CT

    (2020) Transient Delay-Period activity of agranular insular cortex controls working memory maintenance in learning novel tasks

    Neuron 105:934–946.

    https://doi.org/10.1016/j.neuron.2019.12.008

    • PubMed
    • Google Scholar

Acceptance summary:

This work is of interest to neuroscientists in the field of learning and memory as well as those interested in sensory processing and cortico-cortical interactions. It defines reciprocal cortical pathways that differentially support taste novelty behaviour and learning. Overall, the conclusions are well-supported by the data.

Decision letter after peer review:

Thank you for submitting your article "Insula to mPFC Reciprocal Connectivity Differentially Underlies Novel Taste Neophobic Response and Learning" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Laura Colgin as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Shauna L Parkes (Reviewer #1); Jelena Radulovic (Reviewer #2); Steven A Kushner (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission. Overall, the reviewers found the study interesting and the data provided compelling. The reviewers also pointed the following list of items that you need to address in your revised manuscript. For the revised version, please indicate in the title that the biological model was the mouse in this study (this is an eLife's policy).

Essential revisions:

1. Interpretation of Data

Based on the data provided, is the cortical circuit of interest specific to taste? Can we delineate whether the effective circuit manipulations affect the detection of the novel taste or anxiety associated with presentation of this taste, or both? Providing additional behavioral data (not necessary new experiments but new measures – for example on latency to approach the new taste, number of approaches or total time spent sniffing the bottle – which would indicate novelty detection (or lack thereof) independently of saccharose consumption) or a more thorough discussion of these issues is needed.

2. Include BLA data in the main text

The comparison between IC-to-mPFC versus IC-to-BLA is actually a strength of this manuscript. It supports the specificity of the IC-to-mPFC results and allows us to appreciate the broader circuity at work. Please incorporate the data in the main text (so figures as main figures, not supplements) and discuss them accordingly in the manuscript.

3. Scopolamine data

The scopolamine data were found interesting but with specific issues. The authors could decide to remove them entirely from the manuscript to simplify a take-home at the circuit-level. The authors could also keep the data but they will then need to address some issues:

– Can the authors clarify if increased excitability in mPFC-projecting neurons in the IC truly reflects a learning process?

– Figure 3., an alternative interpretation is that mAchR activation may be required at baseline for maintaining intrinsic properties of aIC neurons (F/I curves for novel vs. familiar compared to saline vs. scopolamine are remarkably similar, the underlying physiological mechanism appears divergent, given the relative discordances in RMP, rheobase, input resistance, and AP half-width [Figures 2/3 and S2/S3]).

– Can we really connect slice data with behavioral, circuit-level data here?

4. Controls

Better CNO controls (especially given differences in size groups) are warranted (controls receiving CNO or in vitro slices).

AAV controls; the selectivity of promoters (cell specificity of infected cells) and cre-dependent constructs (i.e. potential leakage of transgene expression without Cre) need to be examined.

Providing baseline levels for pERK (some areas have elevated baseline activity or could be sensitive to the deprivation protocol) and ideally total numbers of AAV+ cells /layer (in addition to the pErk/AAV %) as this will help estimate the variability of labeling between different animals and the overall number of mPFC-projecting neurons would be much appreciated.

5. Figures

Many figures need to have clearer indication of the layers and areas used for quantification (especially 1h, 4h, sup5b)

Reviewer #1:

Kayyal et al. investigated the role of reciprocal pathways between medial prefrontal cortex and insular cortex in novel taste neophobia and memory formation in mice. By combining both correlational (functional neuroanatomy and electrophysiology) and causal techniques (chemogenetics), the authors show that mPFC-projecting neurons in the IC are required for both novel taste expression (neophobia) and taste learning whereas IC-projecting neurons in the mPFC are only involved in the former process. The authors show that consumption of a novel taste increases (1) the number of pERK⁺ neurons in mPFC-projecting neurons in IC and IC-projecting neurons in mPFC, and (2) the excitability of mPFC-projecting neurons in IC, which is also shown to be ACh-dependent. Finally, using a dual-virus chemogenetic approach to target projection-defined neurons, the authors show that inhibition of the IC-to-mPFC pathway hinders both the expression of taste neophobia and its attenuation (i.e., learning). By contrast, the mPFC-to-IC pathway attenuates only the expression of neophobia but not its attenuation. Demonstrating a differential involvement of reciprocally connected cortical regions is innovative and is likely to be of interest to a fairly broad audience in the field of learning and memory.

One of the major strengths of this manuscript is its use of several complementary approaches that, together, inspire a high level of confidence in the results. The authors also include several behavioural and anatomical controls. For instance, the specificity of the IC-to-mPFC in appetitive taste learning is further supported by comparing the involvement of this pathway to the IC-to-BLA pathway, which is shown to be required for taste neophobia but not attenuation of neophobia (i.e., appetitive taste learning).

Overall, the conclusions are well-supported by the data but there are some weaknesses. Most importantly, the extent to which these results are generalizable to other salient stimuli remains unclear and the visual representation and control of the viral expression (for cre-dependent DREADD virus and retro-AAV) is insufficient.

Weaknesses

1. The authors have not sufficiently addressed whether the involvement of this cortical circuitry in salience and novelty processing is strictly limited to taste. The extent to which the reciprocal interactions between mPFC and IC are also needed to "detect, react to, and learn about" other (non-taste related) sensory stimuli remains unclear. While it is understandable that the authors focus their discussion on interactions between IC and mPFC, there is also no discussion (or mention) of the possible involvement of an mPFC-BLA pathway in novel taste behaviour, particularly in the initial expression of a neophobic response. This seems important given the results with quinine (an innately aversive yet still novel taste) indicate that the involvement of the mPFC-IC reciprocal pathways in novel taste behaviour is not ubiquitous.

2. There is no demonstration of the cre-dependency of the AAV construct expressing DREADD at this titre. Also, the visual representation/quantification of the virus expression (both injection sites and retrograde labelling) is insufficient. For example, the injection site of the retro-AAV in Figure 1i appears to predominately target PL but the full extent of the injection site is unclear (same comment for Figure 5a, where M2 also appears to have been targeted). In Figure 4, magnified images of mPFC are shown but we are not provided with a ROI on the map image (Figure 4i). It is also impossible to assess the number of IC projecting neurons in the subregions (PL, Cg, IL). The image provided (Figure 4i) does not actually show IL at this anteroposterior level and there appears to be very little labelling in Cg (although it is difficult to see at this magnification).

3. While an explicit CNO control group is lacking in this paper, any concerns regarding the effect of CNO per se on taste behaviour are somewhat alleviated by the demonstration that CNO injection (inhibition of aIC-mPFC pathway) does not impair taste recognition and retrieval of a familiar taste. Indeed, mice injected with CNO (n=11) but not saline (n=10) show an impaired neophobic response on day 1 (unfamiliar taste) and an increase in the neophobic response on the next (CNO-free) day. When tested on day 7 (when the taste is now familiar), CNO has no effect indicating that recognition and retrieval of a familiar taste is intact. However, on day 7, the group sizes have been reduced to CNO (n=6) and control (n=5). Why are these group sizes different if the same animals were tested? And if these are different mice, why?

4. ACh release in IC is increased following consumption of an unfamiliar taste. The authors showed that novel taste consumption increased excitability in mPFC-projecting neurons in the IC and this excitability was reduced in scopolamine injected mice. However, the authors did not test the mPFC-to-IC pathway. I imagine that the authors would not expect increased excitability in this pathway if its role is limited to expression of neophobia and not learning. But, unfortunately, without this complementary experiment, it remains unclear. Following the same logic, it is unknown if this reduction in excitability is specific to blocking ACh receptors, even if one might expect that it would be ACh-dependent (and NMDA-independent for example) if the increased excitability truly reflects a learning process.

Reviewer #2:

While I am in general enthusiastic about this paper, I have several suggestions that I believe might strengthen the manuscript and provide some clarifications regarding the research topic:

1. The key question is related to the overall interpretation that focuses on salience/novelty detection. I found it difficult to delineate whether the effective circuit manipulations affect the detection of the novel taste or anxiety associated with presentation of this taste, or both. I know that these may not be easy to detangle, but I think that consideration of all of these interpretations is needed to fully understand the behavioral effects during expression of neophobia. This can be addressed to some extent by providing additional behavioral data, for example on latency to approach the new taste, number of approaches or total time spent sniffing the bottle, which would indicate novelty detection (or lack thereof) independently of saccharose consumption. Or with a control group receiving a familiar taste (water) in a novel bottle, etc. The authors may already have performed such control experiments, and if so, I would suggest presenting such data to facilitate interpretation. This issue is especially relevant in view of the findings that impaired "expression of neophobia" can nevertheless result in taste memory formation (mPFC to AIC projection results).

2. I would also find it useful to show the baseline levels of pErk (naïve mice) and after water deprivation only in the AIC and mPFC because some areas have elevated baseline activity or could be sensitive to the deprivation protocol. It would also be helpful to see the total numbers of AAV+ cells /layer (in addition to the pErk/AAV %). This will help estimate the variability of labeling between different animals and the overall number of mPFC-projecting neurons.

3. The first figures need to have clear indication of the layers and areas used for quantification.

4. It would be helpful to demonstrate (and validate) the CNO effects in slices.

5. I found two aspects of the work underdeveloped: scopolamine and BLA. I would suggest removing the BLA data from this report because all of it is in the supplement and then suddenly shows in the main Figure denoting the circuit. Strengthening the AIC-mPFC circuit model would suffice in my view. The cholinergic aspect would need much more work to integrate because the slice data are very interesting, yet there are no supporting behavioral or circuit data deepening the understanding of the AIC inner layer cholinergic system in the observed effects. If such link is not easy to establish, I would also tend to omit these data and maybe replace them with the retrieval findings. Alternatively, the discussion on cholinergic mechanisms needs to be much stronger and in tighter connection with the demonstrated circuit.

Reviewer #3:

The Rosenblum lab has conducted an important series of experiments to dissect the causal influence of the aIC-to-mPFC connection in distinct aspects of novel taste learning and its adaptive attenuation with subsequent repeated exposure (familiar taste learning). They have used a combination of retrograde labeling to enable visualization of bidirectional monosynaptic connectivity between IC and mPFC, together with pERK labeling to infer in vivo neuronal activation, ex vivo whole cell electrophysiological recordings, and DREADD manipulations to establish the causality of defined connections at distinct cognitive phases during novel taste learning and the subsequent transition to neophobic attenuation with repeated exposure. Overall, the results make an important contribution to our understanding of the cognitive function and plasticity this important neural circuit.

The conclusions of this paper are mostly well supported by data, but a few aspects could be further clarified:

1. AAV8-EF1a and rAAV-hSyn1 promotors for reciprocal aIC-to-mPFC connectivity studies. The EF1a and hSyn1 promoters drive overlapping expression in a broad diversity of neuronal cell types, which leaves open the possibility that distinct classes projection neuron types may be involved. This is particularly relevant for interpretation of the result of the pERK labeling and DREADD inhibition experiments.

2. An alternative explanation for the results in Figure 2 of the novel vs. familiar electrophysiological comparison is that familiar saccharin exposure might decrease intrinsic excitability. Similarly, an alternative explanation for the scopolamine results in Figure 3 is that mAchR activation is required at baseline for maintaining intrinsic properties of aIC neurons. This is especially relevant because although the F/I curves for novel vs. familiar compared to saline vs. scopolamine are remarkably similar, the underlying physiological mechanism appears divergent, given the relative discordances in RMP, rheobase, input resistance, and AP half-width (Figures 2/3 and S2/S3).

3. Previous reports have documented the potential confound on behavioral studies with the use of CNO. Therefore, it would be helpful to confirm whether potential actions of CNO on endogenous neurobiology – independent of its function as a ligand for the DREADD receptor – may be influencing results of key experiments in the manuscript. In particular, it is notable that administration of CNO is associated with a consistent decrease in expression of neophobia across multiple experimental conditions (Figures 5, 6 and S5).

Essential revisions:

1. Interpretation of Data

Based on the data provided, is the cortical circuit of interest specific to taste? Can we delineate whether the effective circuit manipulations affect the detection of the novel taste or anxiety associated with presentation of this taste, or both? Providing additional behavioral data (not necessary new experiments but new measures – for example on latency to approach the new taste, number of approaches or total time spent sniffing the bottle – which would indicate novelty detection (or lack thereof) independently of saccharose consumption) or a more thorough discussion of these issues is needed.

We thank the reviewers for this constructive comment.

In our study we tested whether the aIC-mPFC inhibition affects novel taste detection by inhibition of the circuit prior to quinine (novel, innately aversive tastant), which did not affect the neophobic reaction, indicating no changes in novel taste detection.

Following the important comment, we tested whether inhibiting aIC-mPFC pathway affects anxiety levels during an open-field test through testing of exploratory behavior (Seibenhener & Wooten, 2015). Inhibiting aIC-mPFC pathway did not affect the time spent in the center of the arena nor the frequency in crossing the center (new Figure 6—figure supplement 2a, b), indicating no effect on anxiety-related behavior.

Furthermore, we tested the latency towards approaching the novel tastant as suggested by the reviewer, following aIC-mPFC inhibition. No difference was found in latency towards the novel taste nor the water pipettes (new Figure 6—figure supplement 2d,e), indicating no effect on novelty detection or anxiety.

Recent work reported an essential role of mPFC-to-aIC interplay in novel olfactory tasks (Zhu et al., 2020). Although we tested the necessity of aIC-mPFC circuitry in novel taste experience, we do not exclude its involvement in encoding of other sensory stimuli as suggested by the reviewer. Hence, we addressed this important issue in the Discussion section.

2. Include BLA data in the main text

The comparison between IC-to-mPFC versus IC-to-BLA is actually a strength of this manuscript. It supports the specificity of the IC-to-mPFC results and allows us to appreciate the broader circuity at work. Please incorporate the data in the main text (so figures as main figures, not supplements) and discuss them accordingly in the manuscript.

We appreciate the reviewer’s comment, which has led us to greatly improve the flow and clarity of the paper. BLA data was incorporated to the main text as suggested by the reviewers.

3. Scopolamine data

The scopolamine data were found interesting but with specific issues. The authors could decide to remove them entirely from the manuscript to simplify a take-home at the circuit-level. The authors could also keep the data but they will then need to address some issues:

– Can the authors clarify if increased excitability in mPFC-projecting neurons in the IC truly reflects a learning process?

– Figure 3., an alternative interpretation is that mAchR activation may be required at baseline for maintaining intrinsic properties of aIC neurons (F/I curves for novel vs. familiar compared to saline vs. scopolamine are remarkably similar, the underlying physiological mechanism appears divergent, given the relative discordances in RMP, rheobase, input resistance, and AP half-width [Figures 2/3 and S2/S3]).

– Can we really connect slice data with behavioral, circuit-level data here?

We thank the reviewer for raising this important issue, and accordingly following debating, we removed the scopolamine data from the manuscript.

Slice data has its known limitations as mentioned by the reviewer. However, previous published slice electrophysiology data (Bloodgood et al., 2018; Campanac et al., 2013; Dunn et al., 2019; Otis et al., 2018; Pignatelli et al., 2019; Sano et al., 2014; Santini et al., 2008; Santini & Porter, 2010; Sehgal et al., 2014; Soler-Cedeño et al., 2016; Song et al., 2015; Viosca et al., 2009; Wayman & Woodward, 2018; Whitaker et al., 2017; Yiannakas et al., 2021; Yiu et al., 2014), showed an important correlation between circuit level changes and increased excitability and intrinsic properties in specific neurons, following learning processes. Importantly, we are aware of the limitations and continued the examination using causative relation in the behaving mice.

4. Controls

Better CNO controls (especially given differences in size groups) are warranted (controls receiving CNO or in vitro slices).

AAV controls; the selectivity of promoters (cell specificity of infected cells) and cre-dependent constructs (i.e. potential leakage of transgene expression without Cre) need to be examined.

Providing baseline levels for pERK (some areas have elevated baseline activity or could be sensitive to the deprivation protocol) and ideally total numbers of AAV+ cells /layer (in addition to the pErk/AAV %) as this will help estimate the variability of labeling between different animals and the overall number of mPFC-projecting neurons would be much appreciated.

We thank the reviewers for highlighting the need of further extension of our control experiments. In response to these important points raised by the reviewers, we performed additional experiments.

In order to address how CNO administration by itself affect classical neophobia or its attenuation, control mice (injected with retro-Cre AAV construct at the mPFC, and AAV8_hEF1a-dlox-mCherry (rev)-dlox-WPRE-hGHp (A), an mCherry construct without DREADD receptor, at the IC.) were prepared, and similar behavioral taste-tests were performed. There was no difference in aversion towards novel tastant (or its attenuation) following CNO administration in control-virus injected mice and the saline injected group (new Figure 4—figure supplement 1a). This result indicates no effect of CNO in taste behavior (in the concentration we used).

Regarding cell-specificity of infected cells: Indeed, different neuronal populations (excitatory and inhibitory) might have been targeted by our viral vector containing the synapsin promoter. However, from the literature (Baker et al., 2018; Gehrlach et al., 2020; Greig et al., 2013; Molyneaux et al., 2007; Parnavelas, 2000), we know that most cortical projections (>95%) are excitatory neurons. In addition, and in parallel, this is also notable in our electrophysiological measurements by which all neurons patched show a pyramidal-neuron-like properties. Hence, the probability of targeting inhibitory neurons is low to nothing.

As for the concern regarding potential leakage of transgene expression without Cre: we have used all cre-dependent virus -double inverted floxed (AAV8_hEF1a-dlox-hM4D(Gi)_mCherry(rev)-dlox-WPRE-hGHp(A), aiming for high specificity to cre recombination and less likely to the leakage. The expression of the target gene is specific in cells that express Cre recombinase protein. (Tervo et al., 2016; Zerbi et al., 2019).

Following the reviewers' comment and in order to further clarify the aIC-mPFC anatomical connectivity, we measured and provided data for total numbers of rAAV⁺ cells in the different subregions/layers (Figure 1—figure supplementary 1a-d). Number of rAAV+ neurons in the aIC and the mPFC was similar both in novel and familiar saccharin groups, indicating no variability in injections/labeling between the groups.

5. Figures

Many figures need to have clearer indication of the layers and areas used for quantification (especially 1h, 4h, sup5b)

As suggested by the reviewer, we amended the figures to improve clarity. Clearer indication of the areas and layers examined was provided in the figures.

Reviewer #1:

Kayyal et al. investigated the role of reciprocal pathways between medial prefrontal cortex and insular cortex in novel taste neophobia and memory formation in mice. By combining both correlational (functional neuroanatomy and electrophysiology) and causal techniques (chemogenetics), the authors show that mPFC-projecting neurons in the IC are required for both novel taste expression (neophobia) and taste learning whereas IC-projecting neurons in the mPFC are only involved in the former process. The authors show that consumption of a novel taste increases (1) the number of pERK⁺ neurons in mPFC-projecting neurons in IC and IC-projecting neurons in mPFC, and (2) the excitability of mPFC-projecting neurons in IC, which is also shown to be ACh-dependent. Finally, using a dual-virus chemogenetic approach to target projection-defined neurons, the authors show that inhibition of the IC-to-mPFC pathway hinders both the expression of taste neophobia and its attenuation (i.e., learning). By contrast, the mPFC-to-IC pathway attenuates only the expression of neophobia but not its attenuation. Demonstrating a differential involvement of reciprocally connected cortical regions is innovative and is likely to be of interest to a fairly broad audience in the field of learning and memory.

One of the major strengths of this manuscript is its use of several complementary approaches that, together, inspire a high level of confidence in the results. The authors also include several behavioural and anatomical controls. For instance, the specificity of the IC-to-mPFC in appetitive taste learning is further supported by comparing the involvement of this pathway to the IC-to-BLA pathway, which is shown to be required for taste neophobia but not attenuation of neophobia (i.e., appetitive taste learning).

Overall, the conclusions are well-supported by the data but there are some weaknesses. Most importantly, the extent to which these results are generalizable to other salient stimuli remains unclear and the visual representation and control of the viral expression (for cre-dependent DREADD virus and retro-AAV) is insufficient.

Weaknesses

1. The authors have not sufficiently addressed whether the involvement of this cortical circuitry in salience and novelty processing is strictly limited to taste. The extent to which the reciprocal interactions between mPFC and IC are also needed to "detect, react to, and learn about" other (non-taste related) sensory stimuli remains unclear. While it is understandable that the authors focus their discussion on interactions between IC and mPFC, there is also no discussion (or mention) of the possible involvement of an mPFC-BLA pathway in novel taste behaviour, particularly in the initial expression of a neophobic response. This seems important given the results with quinine (an innately aversive yet still novel taste) indicate that the involvement of the mPFC-IC reciprocal pathways in novel taste behaviour is not ubiquitous.

We sincerely thank the reviewer for their valuable comments and suggestions.

Both the IC and the mPFC are heavily implicated in learning of novel sensory stimuli (Bermudez-Rattoni et al., 2005; Gogolla, 2017; Morici et al., 2015; Wang et al., 2020) and the connectivity between them was found to be crucial in learning novel olfactory tasks (Zhu et al., 2020). This indeed raises the possible involvement of aIC-mPFC circuitry in reacting-to and learning of sensory modalities other than taste learning, which we addressed in the Discussion section as suggested by the reviewer.

The mPFC and the BLA are reciprocally and functionally connected (Yizhar & Klavir, 2018). This interplay has been shown to be crucial for learning of valence-specific behaviors (Yizhar & Klavir, 2018) including water intake (Zhao et al., 2020) and in innate fear responses (Jhang et al., 2018). Indeed, it is reasonable to suggest that the interplay between the BLA and the mPFC plays a role in novelty detection and learning as mentioned by the reviewer, and hence we mentioned it in the discussion.

A more thorough debate of the interplay between the BLA and the mPFC was added to the discussion, as well as the involvement of the aIC-mPFC circuit in other modalities.

2. There is no demonstration of the cre-dependency of the AAV construct expressing DREADD at this titre. Also, the visual representation/quantification of the virus expression (both injection sites and retrograde labelling) is insufficient. For example, the injection site of the retro-AAV in Figure 1i appears to predominately target PL but the full extent of the injection site is unclear (same comment for Figure 5a, where M2 also appears to have been targeted). In Figure 4, magnified images of mPFC are shown but we are not provided with a ROI on the map image (Figure 4i). It is also impossible to assess the number of IC projecting neurons in the subregions (PL, Cg, IL). The image provided (Figure 4i) does not actually show IL at this anteroposterior level and there appears to be very little labelling in Cg (although it is difficult to see at this magnification).

We thank the reviewers for raising this important issue. We agree that electrophysiological demonstration and quantification of the effects of CNO in slices expressing inhibitory DREADDs is fundamental to the credibility of the results outlined in the manuscript. We have previously shown a proof of concept for our viral tools in a study published previously (Kayyal et al., 2019) using the rAAV Cre and the cre-dependent DREADDs in IC-to-BLA projecting neurons.

As recommended by the reviewer, we have included overlays confirming that our injection was confined within the aIC or mPFC (Figure 1 and Figure 3), in inhibitory DREADD as well as in correlative pERK experiments. We believe that the above indeed improves the clarity of the presented images as suggested by the reviewer.

3. While an explicit CNO control group is lacking in this paper, any concerns regarding the effect of CNO per se on taste behaviour are somewhat alleviated by the demonstration that CNO injection (inhibition of aIC-mPFC pathway) does not impair taste recognition and retrieval of a familiar taste. Indeed, mice injected with CNO (n=11) but not saline (n=10) show an impaired neophobic response on day 1 (unfamiliar taste) and an increase in the neophobic response on the next (CNO-free) day. When tested on day 7 (when the taste is now familiar), CNO has no effect indicating that recognition and retrieval of a familiar taste is intact. However, on day 7, the group sizes have been reduced to CNO (n=6) and control (n=5). Why are these group sizes different if the same animals were tested? And if these are different mice, why?

Though we used a concentration of CNO considered to be specific, we thank the reviewers for this constructive comment and preform an additional experiment to validate it. Hence, we added a control group, in which mice were injected with retro-Cre AAV construct at the mPFC, and AAV8_hEF1a-dlox-mCherry (rev)-dlox-WPRE-hGHp (A), an mCherry construct without DREADD receptor, at the aIC. Animals were injected with CNO prior to novel taste procedure (Figure 4—figure supplementary 1a) to control for CNO effects. We observed no difference between the control-virus injected mice that were treated with CNO and the aIC-to-mPFC inhibitory DREADDs-injected mice. This rules out the possible involvement of CNO itself on the measured taste behavior.

Regarding the changes in number of mice in the different procedures- The first experiment was done with an n number of (CNO=5, saline=5), without testing the effect on retrieval of familiar taste. In the second batch, we used another set of animals (CNO=6, saline=5), where we wanted to increase the n number of our experiment, keeping in mind we wanted to control this time for taste recognition. Since, there is no difference between the groups, we did not repeat this test.

4. ACh release in IC is increased following consumption of an unfamiliar taste. The authors showed that novel taste consumption increased excitability in mPFC-projecting neurons in the IC and this excitability was reduced in scopolamine injected mice. However, the authors did not test the mPFC-to-IC pathway. I imagine that the authors would not expect increased excitability in this pathway if its role is limited to expression of neophobia and not learning. But, unfortunately, without this complementary experiment, it remains unclear. Following the same logic, it is unknown if this reduction in excitability is specific to blocking ACh receptors, even if one might expect that it would be ACh-dependent (and NMDA-independent for example) if the increased excitability truly reflects a learning process.

We thank the reviewer for pointing this out. Accordingly, we have removed all the scopolamine data from the manuscript. The manuscript is now better focused on the main hypothesis and take-home messages are clearer.

It would have been interesting to explore the changes occurring in the mPFC following novel taste experience, however, it is not within the scope of our study. The main focus in our paper was to better understand the changes in the aIC and the circuits involving it, in novel taste experience.

Reviewer #2:

While I am in general enthusiastic about this paper, I have several suggestions that I believe might strengthen the manuscript and provide some clarifications regarding the research topic:

1. The key question is related to the overall interpretation that focuses on salience/novelty detection. I found it difficult to delineate whether the effective circuit manipulations affect the detection of the novel taste or anxiety associated with presentation of this taste, or both. I know that these may not be easy to detangle, but I think that consideration of all of these interpretations is needed to fully understand the behavioral effects during expression of neophobia. This can be addressed to some extent by providing additional behavioral data, for example on latency to approach the new taste, number of approaches or total time spent sniffing the bottle, which would indicate novelty detection (or lack thereof) independently of saccharose consumption. Or with a control group receiving a familiar taste (water) in a novel bottle, etc. The authors may already have performed such control experiments, and if so, I would suggest presenting such data to facilitate interpretation. This issue is especially relevant in view of the findings that impaired "expression of neophobia" can nevertheless result in taste memory formation (mPFC to AIC projection results).

We would like to thank the reviewer for their important feedback and for bringing this issue to our attention.

To address the reviewer’s comment, we sought to test the effect of the aIC-mPFC inhibition on anxiety levels. We targeted aIC-mPFC pathway, and tested the open-field behavior when the pathway is manipulated similarly to other experiments in the manuscript. The amount of time spent in the center or corners of the chamber provide us a measurement of stress and anxiety levels during inhibition of aIC-mPFC circuitry (new Figure 6—figure supplementary 2a-c). Furthermore, we tested the latency towards the novel tastant during aIC-mPFC inhibition as suggested by the reviewer. Our results show that inhibiting aIC-mPFC circuit does not affect anxiety levels as measured in the open field test nor in latency towards the novel taste tested (new Figure 6—figure supplementary 2a-e).

2. I would also find it useful to show the baseline levels of pErk (naïve mice) and after water deprivation only in the AIC and mPFC because some areas have elevated baseline activity or could be sensitive to the deprivation protocol. It would also be helpful to see the total numbers of AAV+ cells /layer (in addition to the pErk/AAV %). This will help estimate the variability of labeling between different animals and the overall number of mPFC-projecting neurons.

We thank the reviewers for the suggestions aimed to extend our analysis.

Indeed, drinking following water restriction affects gene expression such as Arc and cFos in the IC (Inberg et al., 2016). However, in order to control the baseline levels of pERK, both novel and familiar taste groups were water restricted equally during the behavioral paradigm. Also, mice were provided with an equal amount (1 ml) of saccharin in order to prevent variability in volumes consumed in the last day. This ensures the equal effect of dehydration/drinking in both groups and allows comparison of the familiarity-novelty axis only.

Following the reviewers' comment and in order to further clarify the aIC-mPFC anatomical connectivity, we performed quantification of rAAV⁺ neurons in the aIC and the mPFC and provided the data as a supplementary figure (Figure 1—figure supplement1) to better clarify the variability of labeling between the groups. Number of rAAV+ neurons in the aIC and the mPFC was similar both in novel and familiar saccharin groups, indicating no variability in injections/labeling between the groups.

3. The first figures need to have clear indication of the layers and areas used for quantification.

Thanking the reviewers for the comment. We amended the figures to improve clarity as proposed.

4. It would be helpful to demonstrate (and validate) the CNO effects in slices.

The reviewer raises an important point. We agree that electrophysiological demonstration and quantification of the effects of CNO in slices expressing inhibitory DREADDs is fundamental to the credibility of the results. We have previously shown and validated the effect of CNO in slices (Kayyal et al., 2019) in aIC-to-BLA projecting neurons, using the same viral vectors (rAAV-Cre at the BLA and Cre-dependent DREADDs in aIC).

5. I found two aspects of the work underdeveloped: scopolamine and BLA. I would suggest removing the BLA data from this report because all of it is in the supplement and then suddenly shows in the main Figure denoting the circuit. Strengthening the AIC-mPFC circuit model would suffice in my view. The cholinergic aspect would need much more work to integrate because the slice data are very interesting, yet there are no supporting behavioral or circuit data deepening the understanding of the AIC inner layer cholinergic system in the observed effects. If such link is not easy to establish, I would also tend to omit these data and maybe replace them with the retrieval findings. Alternatively, the discussion on cholinergic mechanisms needs to be much stronger and in tighter connection with the demonstrated circuit.

We thank the reviewers for bringing this idea to our notice. To improve the flow and clarity of the paper, BLA data was incorporated to the main text as suggested by the reviewers. Additionally, scopolamine data was removed from the manuscript.

Reviewer #3:

The Rosenblum lab has conducted an important series of experiments to dissect the causal influence of the aIC-to-mPFC connection in distinct aspects of novel taste learning and its adaptive attenuation with subsequent repeated exposure (familiar taste learning). They have used a combination of retrograde labeling to enable visualization of bidirectional monosynaptic connectivity between IC and mPFC, together with pERK labeling to infer in vivo neuronal activation, ex vivo whole cell electrophysiological recordings, and DREADD manipulations to establish the causality of defined connections at distinct cognitive phases during novel taste learning and the subsequent transition to neophobic attenuation with repeated exposure. Overall, the results make an important contribution to our understanding of the cognitive function and plasticity this important neural circuit.

The conclusions of this paper are mostly well supported by data, but a few aspects could be further clarified:

1. AAV8-EF1a and rAAV-hSyn1 promotors for reciprocal aIC-to-mPFC connectivity studies. The EF1a and hSyn1 promoters drive overlapping expression in a broad diversity of neuronal cell types, which leaves open the possibility that distinct classes projection neuron types may be involved. This is particularly relevant for interpretation of the result of the pERK labeling and DREADD inhibition experiments.

We thank the reviewer for pointing this out. We agree with this comment, while hSyn1 promoter is directed only to neuronal population, EF1a directs a broader cell population. However, in the causative experiments in which we inhibited either aIC-to-mPFC or mPFC-to-aIC circuit, we used a retro-Cre AAV construct: rAAV-hSyn1-chI-EGFP_2A_iCre-WPRE-SV40p(A). meaning the targeting eventually was similar (hSyn1 promoter) both in the correlative and the causative studies.

We know from the literature (Baker et al., 2018; Gehrlach et al., 2020; Greig et al., 2013; Molyneaux et al., 2007; Parnavelas, 2000) that cortical and subcortical projecting neurons are mostly excitatory and not inhibitory neurons (>95%). In addition, our electrophysiological studies show that all neurons patched have pyramidal-neuron-like properties. Hence, the probability of us targeting inhibitory neurons is very small.

2. An alternative explanation for the results in Figure 2 of the novel vs. familiar electrophysiological comparison is that familiar saccharin exposure might decrease intrinsic excitability. Similarly, an alternative explanation for the scopolamine results in Figure 3 is that mAchR activation is required at baseline for maintaining intrinsic properties of aIC neurons. This is especially relevant because although the F/I curves for novel vs. familiar compared to saline vs. scopolamine are remarkably similar, the underlying physiological mechanism appears divergent, given the relative discordances in RMP, rheobase, input resistance, and AP half-width (Figures 2/3 and S2/S3).

In response to this important point raised by the reviewer, we have performed an additional experiment to examine the directionality of novel/familiar taste experience upon aIC-to-mPFC projecting neurons by adding a home-caged group of mice (Figure 2; Figure 2—figure supplement 2). Our results show a similar excitability levels between home-caged group and familiar saccharin group that are different from novel saccharin group, meaning the effect is derived from the novelty of the taste (increase excitability in aIC-to-mPFC projections).

In addition, the scopolamine data was removed as suggested by the reviewers.

3. Previous reports have documented the potential confound on behavioral studies with the use of CNO. Therefore, it would be helpful to confirm whether potential actions of CNO on endogenous neurobiology – independent of its function as a ligand for the DREADD receptor – may be influencing results of key experiments in the manuscript. In particular, it is notable that administration of CNO is associated with a consistent decrease in expression of neophobia across multiple experimental conditions (Figures 5, 6 and S5).

We thank the reviewer for raising this important concern.

Doses of CNO used in all experiments were chosen based on recent publications demonstrating that higher chronic doses of the ligand are metabolized into clozapine, which can affect behavior (Gomez et al., 2017).

To directly address whether CNO administration by itself affect classical neophobia or its attenuation, we performed an additional experiment. Control mice (injected with control virus without DREADDs expression at the IC) were prepared, and similar behavioral taste-tests were performed. Our results indicate that the expression of neophobia is unchanged by CNO administration when using control virus (which does not harbor the receptor). This rule out the possibility that the measured behavior was affected by the CNO itself (Figure 4—figure supplement 1a).

References:

Baker, A., Kalmbach, B., Morishima, M., Kim, J., Juavinett, A., Li, N., and Dembrow, N. (2018). Specialized subpopulations of deep-layer pyramidal neurons in the neocortex: Bridging cellular properties to functional consequences. Journal of Neuroscience, 38(24), 5441–5455. https://doi.org/10.1523/JNEUROSCI.0150-18.2018

Bermudez-Rattoni, F., Okuda, S., Roozendaal, B., and McGaugh, J. L. (2005). Insular cortex is involved in consolidation of object recognition memory. Learning and Memory, 12(5), 447–449. https://doi.org/10.1101/lm.97605

Bloodgood, D. W., Sugam, J. A., Holmes, A., and Kash, T. L. (2018). Fear extinction requires infralimbic cortex projections to the basolateral amygdala. Translational Psychiatry, 8(1). https://doi.org/10.1038/s41398-018-0106-x

Campanac, E., Gasselin, C., Baude, A., Rama, S., Ankri, N., and Debanne, D. (2013). Enhanced Intrinsic Excitability in Basket Cells Maintains Excitatory-Inhibitory Balance in Hippocampal Circuits. Neuron, 77(4), 712–722. https://doi.org/10.1016/j.neuron.2012.12.020

Dunn, A. R., Neuner, S. M., Ding, S., Hope, K. A., M.S., K. O., and Kaczorowski, C. C. (2019). Cell-Type-Specific Changes in Intrinsic Excitability in the Subiculum following Learning and Exposure to Novel Environmental Contexts. ENeuro, 5(6), 18.2018. https://doi.org/10.1523/ENEURO.0484-18.2018

Gehrlach, D. A., Weiand, C., Gaitanos, T. N., Cho, E., Klein, A. S., Hennrich, A. A., Conzelmann, K. K., and Gogolla, N. (2020). A whole-brain connectivity map of mouse insular cortex. eLife, 9, 1–78. https://doi.org/10.7554/eLife.55585

Gogolla, N. (2017). The insular cortex. In Current Biology (Vol. 27, Issue 12, pp. R580–R586). Cell Press. https://doi.org/10.1016/j.cub.2017.05.010

Gomez, J. L., Bonaventura, J., Lesniak, W., Mathews, W. B., Sysa-Shah, P., Rodriguez, L. A., Ellis, R. J., Richie, C. T., Harvey, B. K., Dannals, R. F., Pomper, M. G., Bonci, A., and Michaelides, M. (2017). Chemogenetics revealed: DREADD occupancy and activation via converted clozapine. Science, 357(6350), 503–507. https://doi.org/10.1126/science.aan2475

Greig, L. C., Woodworth, M. B., Galazo, M. J., Padmanabhan, H., and Macklis, J. D. (2013). Molecular logic of neocortical projection neuron specification, development and diversity. NATURE REVIEWS | NEUROSCIENCE, 14, 755. https://doi.org/10.1038/nrn3586

Inberg, S., Jacob, E., Elkobi, A., Edry, E., Rappaport, A., Simpson, T. I., Armstrong, J. D., Shomron, N., Pasmanik-Chor, M., and Rosenblum, K. (2016). Fluid consumption and taste novelty determines transcription temporal dynamics in the gustatory cortex. Molecular Brain, 9(1). https://doi.org/10.1186/s13041-016-0188-4

Jhang, J., Lee, H., Kang, M. S., Lee, H. S., Park, H., and Han, J. H. (2018). Anterior cingulate cortex and its input to the basolateral amygdala control innate fear response. Nature Communications, 9(1). https://doi.org/10.1038/s41467-018-05090-y

Kayyal, H., Yiannakas, A., Kolatt Chandran, S., Khamaisy, M., Sharma, V., and Rosenblum, K. (2019). Activity of Insula to Basolateral Amygdala Projecting Neurons is Necessary and Sufficient for Taste Valence Representation. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 39(47), 9369–9382. https://doi.org/10.1523/JNEUROSCI.0752-19.2019

Molyneaux, B. J., Arlotta, P., L Menezes, J. R., and Macklis, J. D. (2007). Neuronal subtype specification in the cerebral cortex. NATURE REVIEWS | NEUROSCIENCE, 8. https://doi.org/10.1038/nrn2151

Morici, J. F., Bekinschtein, P., and Weisstaub, N. V. (2015). Medial prefrontal cortex role in recognition memory in rodents. In Behavioural Brain Research (Vol. 292, pp. 241–251). Elsevier. https://doi.org/10.1016/j.bbr.2015.06.030

Otis, J. M., Fitzgerald, M. K., Yousuf, H., Burkard, J. L., Drake, M., and Mueller, D. (2018). Prefrontal neuronal excitability maintains cocaine-associated memory during retrieval. Frontiers in Behavioral Neuroscience, 12(June), 1–11. https://doi.org/10.3389/fnbeh.2018.00119

Parnavelas, J. G. (2000). The origin and migration of cortical neurones: New vistas. In Trends in Neurosciences (Vol. 23, Issue 3, pp. 126–131). Trends Neurosci. https://doi.org/10.1016/S0166-2236(00)01553-8

Pignatelli, M., Ryan, T. J., Roy, D. S., Lovett, C., Smith, L. M., Muralidhar, S., and Tonegawa, S. (2019). Engram Cell Excitability State Determines the Efficacy of Memory Retrieval. Neuron, 101(2), 274-284.e5. https://doi.org/10.1016/j.neuron.2018.11.029

Sano, Y., Shobe, J. L., Zhou, M., Huang, S., Shuman, T., Cai, D. J., Golshani, P., Kamata, M., and Silva, A. J. (2014). CREB regulates memory allocation in the insular cortex. Current Biology, 24(23), 2833–2837. https://doi.org/10.1016/j.cub.2014.10.018

Santini, E., and Porter, J. T. (2010). M-Type Potassium Channels Modulate the Intrinsic Excitability of Infralimbic Neurons and Regulate Fear Expression and Extinction. 30(37), 12379–12386.

Santini, E., Quirk, G. J., and Porter, J. T. (2008). Fear Conditioning and Extinction Differentially Modify the Intrinsic Excitability of Infralimbic Neurons. Journal of Neuroscience, 28(15), 4028–4036.

Sehgal, M., Ehlers, V. L., and Moyer James R, J. (2014). Learning enhances intrinsic excitability in a subset of lateral amygdala neurons. Learning and Memory (Cold Spring Harbor, N.Y.), 21(3), 161–170. https://doi.org/10.1101/lm.032730.113

Seibenhener, M. L., and Wooten, M. C. (2015). Use of the open field maze to measure locomotor and anxiety-like behavior in mice. Journal of Visualized Experiments, 96. https://doi.org/10.3791/52434

Soler-Cedeño, O., Cruz, E., Criado-Marrero, M., and Porter, J. T. (2016). Contextual fear conditioning depresses infralimbic excitability. Neurobiology of Learning and Memory, 130(February), 77–82. https://doi.org/10.1016/j.nlm.2016.01.015

Song, C., Ehlers, V. L., Moyer, J. R., and R, J. M. J. (2015). Trace Fear Conditioning Differentially Modulates Intrinsic Excitability of Medial Prefrontal Cortex-Basolateral Complex of Amygdala Projection Neurons in Infralimbic and Prelimbic Cortices. 35(39), 13511–13524.

Tervo, D. G. R., Hwang, B. Y., Viswanathan, S., Gaj, T., Lavzin, M., Ritola, K. D., Lindo, S., Michael, S., Kuleshova, E., Ojala, D., Huang, C. C., Gerfen, C. R., Schiller, J., Dudman, J. T., Hantman, A. W., Looger, L. L., Schaffer, D. V., and Karpova, A. Y. (2016). A Designer AAV Variant Permits Efficient Retrograde Access to Projection Neurons. Neuron, 92(2), 372–382. https://doi.org/10.1016/j.neuron.2016.09.021

Viosca, J., De Armentia, M. L., Jancic, D., and Barco, A. (2009). Enhanced CREB-dependent gene expression increases the excitability of neurons in the basal amygdala and primes the consolidation of contextual and cued fear memory. Learning and Memory, 16(3), 193–197. https://doi.org/10.1101/lm.1254209

Wang, P. Y., Boboila, C., Chin, M., Higashi-Howard, A., Shamash, P., Wu, Z., Stein, N. P., Abbott, L. F., and Axel, R. (2020). Transient and Persistent Representations of Odor Value in Prefrontal Cortex. Neuron, 108(1), 209-224.e6. https://doi.org/10.1016/j.neuron.2020.07.033

Wayman, W. N., and Woodward, J. J. (2018). Chemogenetic excitation of accumbens-projecting infralimbic cortical neurons blocks toluene-induced conditioned place preference. Journal of Neuroscience, 38(6), 1462–1471. https://doi.org/10.1523/JNEUROSCI.2503-17.2018

Whitaker, L. R., Warren, B. L., Venniro, M., Harte, T. C., McPherson, K. B., Beidel, J., Bossert, J. M., Shaham, Y., Bonci, A., and Hope, B. T. (2017). Bidirectional modulation of intrinsic excitability in rat prelimbic cortex neuronal ensembles and non-ensembles after operant learning. Journal of Neuroscience, 37(36), 8845–8856. https://doi.org/10.1523/JNEUROSCI.3761-16.2017

Yiannakas, A., Kolatt Chandran, S., Kayyal, H., Gould, N., Khamaisy, M., and Rosenblum, K. (2021). Parvalbumin interneuron inhibition onto anterior insula neurons projecting to the basolateral amygdala drives aversive taste memory retrieval. Current Biology, 1–15. https://doi.org/10.1016/j.cub.2021.04.010

Yiu, A. P., Mercaldo, V., Yan, C., Richards, B., Rashid, A. J., Hsiang, H. L. L., Pressey, J., Mahadevan, V., Tran, M. M., Kushner, S. A., Woodin, M. A., Frankland, P. W., and Josselyn, S. A. (2014). Neurons Are Recruited to a Memory Trace Based on Relative Neuronal Excitability Immediately before Training. Neuron, 83(3), 722–735. https://doi.org/10.1016/j.neuron.2014.07.017

Yizhar, O., and Klavir, O. (2018). Reciprocal amygdala–prefrontal interactions in learning. In Current Opinion in Neurobiology (Vol. 52, pp. 149–155). Elsevier Ltd. https://doi.org/10.1016/j.conb.2018.06.006

Zerbi, V., Floriou-Servou, A., Markicevic, M., Vermeiren, Y., Sturman, O., Privitera, M., von Ziegler, L., Ferrari, K. D., Weber, B., De Deyn, P. P., Wenderoth, N., and Bohacek, J. (2019). Rapid Reconfiguration of the Functional Connectome after Chemogenetic Locus Coeruleus Activation. Neuron, 103(4), 702-718.e5. https://doi.org/10.1016/j.neuron.2019.05.034

Zhao, Z., Soria-Gómez, E., Varilh, M., Covelo, A., Julio-Kalajzić, F., Cannich, A., Castiglione, A., Vanhoutte, L., Duveau, A., Zizzari, P., Beyeler, A., Cota, D., Bellocchio, L., Busquets-Garcia, A., and Marsicano, G. (2020). A Novel Cortical Mechanism for Top-Down Control of Water Intake. Current Biology, 30(23), 4789-4798.e4. https://doi.org/10.1016/j.cub.2020.09.011

Zhu, J., Cheng, Q., Chen, Y., Fan, H., Han, Z., Hou, R., Chen, Z., and Li, C. T. (2020). Transient Delay-Period Activity of Agranular Insular Cortex Controls Working Memory Maintenance in Learning Novel Tasks. Neuron, 105(5), 934-946.e5. https://doi.org/10.1016/j.neuron.2019.12.008

Author details

1. Haneen Kayyal

   Sagol Department of Neuroscience, University of Haifa, Mount Carmel, Israel

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4429-3514

2. Sailendrakumar Kolatt Chandran

   Sagol Department of Neuroscience, University of Haifa, Mount Carmel, Israel

   Contribution

   Data curation, Formal analysis, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9805-8096

3. Adonis Yiannakas

   Sagol Department of Neuroscience, University of Haifa, Mount Carmel, Israel

   Contribution

   Data curation, Formal analysis, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

4. Nathaniel Gould

   Sagol Department of Neuroscience, University of Haifa, Mount Carmel, Israel

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

5. Mohammad Khamaisy

   Sagol Department of Neuroscience, University of Haifa, Mount Carmel, Israel

   Contribution

   Data curation, Formal analysis, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

6. Kobi Rosenblum

   1. Sagol Department of Neuroscience, University of Haifa, Mount Carmel, Israel
   2. Center for Gene Manipulation in the Brain, University of Haifa, Mount Carmel, Israel

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Writing - original draft, Writing - review and editing

   For correspondence

   kobir@psy.haifa.ac.il

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4827-0336

• 1,855

  Page views

• 258

  Downloads

• 5

  Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.