Physiological needs bias perception and attention to relevant sensory cues. This process is ‘hijacked’ by drug addiction, causing cue-induced cravings and relapse. Similarly, its dysregulation contributes to failed diets, obesity, and eating disorders. Neuroimaging studies in humans have implicated insular cortex in these phenomena. However, it remains unclear how ‘cognitive’ cortical representations of motivationally relevant cues are biased by subcortical circuits that drive specific motivational states. Here we develop a microprism-based cellular imaging approach to monitor visual cue responses in the insular cortex of behaving mice across hunger states. Insular cortex neurons demonstrate food-cue-biased responses that are abolished during satiety. Unexpectedly, while multiple satiety-related visceral signals converge in insular cortex, chemogenetic activation of hypothalamic ‘hunger neurons’ (expressing agouti-related peptide (AgRP)) bypasses these signals to restore hunger-like response patterns in insular cortex. Circuit mapping and pathway-specific manipulations uncover a pathway from AgRP neurons to insular cortex via the paraventricular thalamus and basolateral amygdala. These results reveal a neural basis for state-specific biased processing of motivationally relevant cues.
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Frank, S., Kullmann, S. & Veit, R. Food related processes in the insular cortex. Front. Hum. Neurosci. 7, 499 (2013)
Naqvi, N. H., Gaznick, N., Tranel, D. & Bechara, A. The insula: a critical neural substrate for craving and drug seeking under conflict and risk. Ann. NY Acad. Sci. 1316, 53–70 (2014)
Cechetto, D. F. & Saper, C. B. Evidence for a viscerotopic sensory representation in the cortex and thalamus in the rat. J. Comp. Neurol. 262, 27–45 (1987)
de Araujo, I. E. et al. Neural ensemble coding of satiety states. Neuron 51, 483–494 (2006)
Hanamori, T., Kunitake, T., Kato, K. & Kannan, H. Responses of neurons in the insular cortex to gustatory, visceral, and nociceptive stimuli in rats. J. Neurophysiol. 79, 2535–2545 (1998)
Huerta, C. I., Sarkar, P. R., Duong, T. Q., Laird, A. R. & Fox, P. T. Neural bases of food perception: coordinate-based meta-analyses of neuroimaging studies in multiple modalities. Obesity (Silver Spring) 22, 1439–1446 (2014)
Cornier, M. A. et al. The effects of overfeeding on the neuronal response to visual food cues in thin and reduced-obese individuals. PLoS ONE 4, e6310 (2009)
Schienle, A., Schäfer, A., Hermann, A. & Vaitl, D. Binge-eating disorder: reward sensitivity and brain activation to images of food. Biol. Psychiatry 65, 654–661 (2009)
Gardner, M. P. & Fontanini, A. Encoding and tracking of outcome-specific expectancy in the gustatory cortex of alert rats. J. Neurosci. 34, 13000–13017 (2014)
Kusumoto-Yoshida, I., Liu, H., Chen, B. T., Fontanini, A. & Bonci, A. Central role for the insular cortex in mediating conditioned responses to anticipatory cues. Proc. Natl Acad. Sci. USA 112, 1190–1195 (2015)
Saddoris, M. P., Holland, P. C. & Gallagher, M. Associatively learned representations of taste outcomes activate taste-encoding neural ensembles in gustatory cortex. J. Neurosci. 29, 15386–15396 (2009)
Samuelsen, C. L., Gardner, M. P. & Fontanini, A. Effects of cue-triggered expectation on cortical processing of taste. Neuron 74, 410–422 (2012)
Parkes, S. L. & Balleine, B. W. Incentive memory: evidence the basolateral amygdala encodes and the insular cortex retrieves outcome values to guide choice between goal-directed actions. J. Neurosci. 33, 8753–8763 (2013)
Aponte, Y., Atasoy, D. & Sternson, S. M. AGRP neurons are sufficient to orchestrate feeding behavior rapidly and without training. Nat. Neurosci. 14, 351–355 (2011)
Krashes, M. J. et al. Rapid, reversible activation of AgRP neurons drives feeding behavior in mice. J. Clin. Invest. 121, 1424–1428 (2011)
Burgess, C. R. et al. Hunger-dependent enhancement of food cue responses in mouse postrhinal cortex and lateral amygdala. Neuron 91, 1154–1169 (2016)
Saper, C. B. The central autonomic nervous system: conscious visceral perception and autonomic pattern generation. Annu. Rev. Neurosci. 25, 433–469 (2002)
Accolla, R. & Carleton, A. Internal body state influences topographical plasticity of sensory representations in the rat gustatory cortex. Proc. Natl Acad. Sci. USA 105, 4010–4015 (2008)
Chen, X., Gabitto, M., Peng, Y., Ryba, N. J. & Zuker, C. S. A gustotopic map of taste qualities in the mammalian brain. Science 333, 1262–1266 (2011)
Andermann, M. L. et al. Chronic cellular imaging of entire cortical columns in awake mice using microprisms. Neuron 80, 900–913 (2013)
Katz, D. B., Simon, S. A. & Nicolelis, M. A. Dynamic and multimodal responses of gustatory cortical neurons in awake rats. J. Neurosci. 21, 4478–4489 (2001)
Reimer, J. et al. Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex. Nat. Commun. 7, 13289 (2016)
Betley, J. N. et al. Neurons for hunger and thirst transmit a negative-valence teaching signal. Nature 521, 180–185 (2015)
Chen, Y., Lin, Y. C., Kuo, T. W. & Knight, Z. A. Sensory detection of food rapidly modulates arcuate feeding circuits. Cell 160, 829–841 (2015)
Mandelblat-Cerf, Y. et al. Arcuate hypothalamic AgRP and putative POMC neurons show opposite changes in spiking across multiple timescales. eLife 4, e07122 (2015)
Huber, D. et al. Multiple dynamic representations in the motor cortex during sensorimotor learning. Nature 484, 473–478 (2012)
Oliveira-Maia, A. J. et al. The insular cortex controls food preferences independently of taste receptor signaling. Front. Syst. Neurosci. 6, 5 (2012)
Petreanu, L., Huber, D., Sobczyk, A. & Svoboda, K. Channelrhodopsin-2-assisted circuit mapping of long-range callosal projections. Nat. Neurosci. 10, 663–668 (2007)
Lo, L. & Anderson, D. J. A. A Cre-dependent, anterograde transsynaptic viral tracer for mapping output pathways of genetically marked neurons. Neuron 72, 938–950 (2011)
Baxter, M. G. & Murray, E. A. The amygdala and reward. Nat. Rev. Neurosci. 3, 563–573 (2002)
Morrison, S. E. & Salzman, C. D. Re-valuing the amygdala. Curr. Opin. Neurobiol. 20, 221–230 (2010)
Kim, J., Pignatelli, M., Xu, S., Itohara, S. & Tonegawa, S. Antagonistic negative and positive neurons of the basolateral amygdala. Nat. Neurosci. 19, 1636–1646 (2016)
Namburi, P. et al. A circuit mechanism for differentiating positive and negative associations. Nature 520, 675–678 (2015)
Stuber, G. D. et al. Excitatory transmission from the amygdala to nucleus accumbens facilitates reward seeking. Nature 475, 377–380 (2011)
Betley, J. N., Cao, Z. F., Ritola, K. D. & Sternson, S. M. Parallel, redundant circuit organization for homeostatic control of feeding behavior. Cell 155, 1337–1350 (2013)
Kirouac, G. J. Placing the paraventricular nucleus of the thalamus within the brain circuits that control behavior. Neurosci. Biobehav. Rev. 56, 315–329 (2015)
Labouèbe, G., Boutrel, B., Tarussio, D. & Thorens, B. Glucose-responsive neurons of the paraventricular thalamus control sucrose-seeking behavior. Nat. Neurosci. 19, 999–1002 (2016)
Stratford, T. R. & Wirtshafter, D. Injections of muscimol into the paraventricular thalamic nucleus, but not mediodorsal thalamic nuclei, induce feeding in rats. Brain Res. 1490, 128–133 (2013)
Zhu, Y., Wienecke, C. F., Nachtrab, G. & Chen, X. A thalamic input to the nucleus accumbens mediates opiate dependence. Nature 530, 219–222 (2016)
Do-Monte, F. H., Quiñones-Laracuente, K. & Quirk, G. J. A temporal shift in the circuits mediating retrieval of fear memory. Nature 519, 460–463 (2015)
Penzo, M. A. et al. The paraventricular thalamus controls a central amygdala fear circuit. Nature 519, 455–459 (2015)
Piette, C. E., Baez-Santiago, M. A., Reid, E. E., Katz, D. B. & Moran, A. Inactivation of basolateral amygdala specifically eliminates palatability-related information in cortical sensory responses. J. Neurosci. 32, 9981–9991 (2012)
Paxinos, G . & Franklin, K. B. J. The Mouse Brain in Stereotaxic Coordinates (Elsevier Academic Press, 2004)
Garfield, A. S. et al. Dynamic GABAergic afferent modulation of AgRP neurons. Nat. Neurosci. 19, 1628–1635 (2016)
Petreanu, L. et al. Activity in motor-sensory projections reveals distributed coding in somatosensation. Nature 489, 299–303 (2012)
Chen, T. W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013)
Aronoff, R. et al. Long-range connectivity of mouse primary somatosensory barrel cortex. Eur. J. Neurosci. 31, 2221–2233 (2010)
Wickersham, I. R. et al. Monosynaptic restriction of transsynaptic tracing from single, genetically targeted neurons. Neuron 53, 639–647 (2007)
Atasoy, D. et al. A genetically specified connectomics approach applied to long-range feeding regulatory circuits. Nat. Neurosci. 17, 1830–1839 (2014)
We thank S. Subramanian, N. Patel, M. Gyetvan, G. Niyazov, D. Anderson, and M. Dello Russo (mouse training), and A. Sugden (electronics). We thank the Lowell laboratory, Andermann laboratory, D. Nachmani, T. Anthony, and J. Assad for discussions. We thank the GENIE Project, Howard Hughes Medical Institute, for GCaMP6. The HSV129∆TK-TT was provided by the Center for Neuroanatomy with Neurotropic Viruses (grant P40RR018604). Authors were supported by a European Molecular Biology Organization postdoctoral fellowship; Edmond and Lily Safra Center for Brain Sciences postdoctoral award (Y.L.); Davis Family Foundation postdoctoral fellowship (C.R.B.); National Science Foundation Graduate Research Fellowship Program and the Sackler Scholars Program (N.J.); National Institutes of Health (NIH), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) NRSA F31 DK105678 (R.N.R.); F32 DK103387 (J.M.R.); an NIH New Innovator Award DP2 DK105570 and R01 DK109930, the Klarman Family Foundation, a McKnight Scholar Award, a Pew Scholar Award and a Smith Family Foundation Award (M.L.A.); NIH R01s DK075632, DK096010, DK089044, DK111401, and P30s DK046200 and DK057521 (B.B.L.).
The authors declare no competing financial interests.
Reviewer Information Nature thanks I. de Araujo, R. Palmiter and the other anonymous reviewer(s) for their contribution to the peer review of this work.
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
a, Summary of task performance across mice, during hunger and satiety. Hit rate: fraction of food cue trials with correct behavioural response (licking within the 2 s response window after cue offset). False alarm rate: fraction of non-food-cue trials in which mice incorrectly licked within the 2 s response window after cue offset. Each dot represents one mouse; lines connect two same-day blocks from the same mouse. *P = 1.8 × 10−5, NS, not significant (P = 0.09); paired t-test, n = 6 mice. Mean ± s.e.m. b, Summary of task performance after InsCtx silencing in hungry mice: false alarm rate and d′ (sensitivity index, which combines hit rate and false alarm rate in a general measure of discrimination), combining both aversive cue and neutral cue trials. Each dot represents one mouse; lines connect two same-session blocks of trials from the same mouse (first day: block 1, saline; block 2, saline; all other days: block 1, saline; block 2, drug). Notice that pharmacological silencing of InsCtx, but not of secondary somatosensory cortex (S2), reduces performance in the visual discrimination task (d′) because of reduced hit rates (Fig. 1c), without significantly affecting false alarm rates. Bars and error bars, mean ± s.e.m.; NS, not significant (P ≥ 0.2); *P = 0.01; **P = 0.007; paired t-test, n = 3 mice. Mean ± s.e.m. c, False alarm rates for aversive cues trials (left) and neutral cue trials (right). Each dot represents one mouse; lines connect two same-session blocks of trials from the same mouse (first day: block 1, saline; block 2, saline; all other days: block 1, saline; block 2, drug). Notice that pharmacological silencing of InsCtx did not significantly affect false alarm rates for aversive cue trials or for neutral cue trials. NS, not significant (P ≥ 0.06); paired t-test, n = 3 mice. Mean ± s.e.m. d, Movement velocity and total distance travelled in the home cage after saline or drug infusion into InsCtx. Note that pharmacological silencing of InsCtx did not affect general locomotion in the home cage. NS, not significant (P ≥ 0.34); paired t-test, n = 3 mice. Mean ± s.e.m.
Extended Data Figure 2 InsCtx activity from an example field of view; most food-cue-responsive neurons also respond to licking/Ensure; localizing imaging fields of view relative to vasculature; no spatial organization of InsCtx food cue and licking/Ensure responses.
a, Example two-photon image (same mouse as in Fig. 1f) containing ~150 active neurons. Scale bar, 100 μm. Right: example activity of 20 neurons (labelled in the left panel) from the same field of view during the behavioural task (vertical bars: presentation of visual cues). Scale bars, 10 s and 100% (ΔF/F). Note that GCaMP dynamics reflect the high ongoing firing rates of mouse InsCtx neurons in vivo (6–10 Hz on average; ref. 10). b, Heatmaps of average responses of all food-cue-responsive neurons meeting the criteria for the clustering analysis (each row is the average activity of a single neuron). Neurons were clustered on the basis of their food cue and licking responses (see Methods). Different heatmaps were aligned to different events (food cue onset, licking onset, Ensure delivery), and all responses were computed relative to the pre-cue period. Notice that many food-cue-responsive neurons also responded to licking/Ensure. c, Post-mortem analysis of the location of the microprism and imaging field of view. Top left: side-view image of the brain of a mouse implanted with a microprism. Blue rectangle shows location of microprism imaging face, identified by a minor indentation of the brain. Top right: post-mortem epifluorescence image of GCaMP6f in lateral cortex. Blue rectangle shows microprism location. Bottom right: in vivo epifluorescence image of GCaMP6f through the microprism at the imaging field of view location. Bottom left: in vivo two-photon field of view, imaged through the microprism. The image was adjusted to enhance the visibility of the neuropil and vasculature. Arrows point to the same vascular landmark in different images. d, Left: schematic side view of the mouse brain. Right: anatomical location of all imaged neurons (dots) across mice, relative to the junction of caudal rhinal vein and middle cerebral artery. Magenta: food-cue-responsive neurons; green: licking and/or Ensure-responsive neurons; grey: unresponsive neurons; dashed rectangles: borders of each imaging field. Scale bar, 0.5 mm. e, Fraction of food-cue-responsive neurons and of licking and/or Ensure-responsive neurons in granular (GI) versus dysgranular (DI) subregions of InsCtx. f, Example field of view from one mouse. Neuron locations are marked with coloured circles (magenta: food-cue-responsive; green: licking/Ensure-responsive only; grey: unresponsive). Scale bar, 100 μm. g, Left: distribution of distances between pairs of neurons in which (1) both neurons were responsive to the food cue (magenta), (2) both neurons were responsive to licking/Ensure (green), and (3) one neuron was responsive to the food cue and the other neuron is responsive to licking/Ensure (black). Note that all three distributions are similar, suggesting no spatial clustering. Right: distribution of distances between pairs of neurons having the same type of response (that is, either both responding to the food cue or both responding to licking/Ensure, blue), and between pairs of neurons with different response types (for example, one neuron responding to the food cue and the other responding to licking/Ensure, black). Note that these distributions are similar, suggesting no spatial clustering.
Extended Data Figure 3 Further analyses of InsCtx cue responses; InsCtx food cue bias emerges after learning and is not dependent on cue orientation or on overexposure to the food cue during early training.
a, Separate analysis of the fraction of cue-responsive neurons for neurons that were either excited (left) or suppressed (right) by cue presentation. Note the stronger food cue bias in neurons with cue-evoked excitation. b, Separate analysis of the response magnitude in excited and suppressed neurons, across hunger and satiety. Note the stronger food cue bias in excited neurons, but no food cue bias during satiety in both groups. Also, note that the responses to the aversive and neutral cues are significantly different only in excited neurons. Excited: *P = 0.002 (Hungry); NS, not significant (P = 0.3, Sated); Kruskal–Wallis test. Pairwise comparisons (Hungry): food cue versus aversive cue, P = 0.04; food cue versus neutral cue, P = 0.003; aversive cue versus neutral cue, P = 0.02. Pairwise comparisons (Hungry versus Sated): ***P ≤ 6 × 10−4 for all three cues; Mann–Whitney U-test (n = 113, 20, and 8 neurons responding to the food, aversive, and neutral cues, respectively; from six mice). Suppressed: *P = 2.4 × 10−5 (Hungry); NS, not significant (P = 0.2, Sated); Kruskal–Wallis test. Pairwise comparisons (Hungry): food cue versus aversive cue, P = 8.3 × 10−4; food cue versus neutral cue, P = 5.6 × 10−5; aversive cue versus neutral cue, P = 0.3. Pairwise comparisons (Hungry versus Sated): ***P ≤ 7 × 10−17 for all three cues; Mann–Whitney U-test (n = 171, 75, and 77 neurons responding to the food, aversive, and neutral cues, respectively; from six mice). Mean ± s.e.m. c, Separate analysis of the cue response selectivity of neurons that were excited or suppressed by cue presentation. Each circle represents all excited/suppressed neurons that were responsive to a given visual cue. Note the higher selectivity in excited neurons. See b for sample sizes. d, Analysis of the response sign (excited/suppressed) of neurons that significantly responded to two cues. Fraction of neurons that significantly responded with the same sign to two different cues, out of the total neurons that responded to the two cues with the same or opposite sign (for example: red, number of neurons excited by food cue and aversive cue out of all neurons excited by the food cue and either excited or suppressed by aversive cue; blue, number of neurons suppressed by food cue and aversive cue out of all neurons suppressed by the food cue and either excited or suppressed by aversive cue). Note that only a very small fraction of neurons excited by the food cue were also excited by one of the other two cues (that is, they were suppressed by the other two cues). Also, note that a substantial fraction of neurons significantly suppressed by one cue would also be suppressed by one or more other cues. e, Analysis of average responses across all neurons for each of the first 20 presentations of each cue within a session. For each neuron with a significant response to a given cue, cue responses were normalized by their mean across the session. These normalized cue responses were then averaged across neurons per trial. Note that, at the population level, cue responses did not show substantial differences between early and later trials. Mean ± s.e.m. f, Fano factor as a measure of trial-to-trial cue response variability across states. Note that, during hunger but not during satiety, the Fano factor was significantly higher for responses to the food cue than for responses to the other two cues. *P = 0.01 (Hungry); NS, not significant (P = 0.4, Sated); Kruskal–Wallis test. Pairwise comparisons (Hungry): food cue versus aversive cue, P = 0.007; food cue versus neutral cue, P = 0.04; aversive cue versus neutral cue, P = 0.7. Pairwise comparisons (Hungry versus Sated): P = 2 × 10−5 for the food cue; P = 0.2 for the aversive and neutral cues; Mann–Whitney U-test (n = 274, 95, and 85 neurons responding to the food, aversive, and neutral cues, respectively; from six mice). Mean ± s.e.m. g, No food cue bias before learning. Fraction of responsive neurons (left), response magnitude (middle), and response selectivity (right) in naive mice before learning the visual discrimination task. Mice were imaged while in a hungry state (85% ad libitum body weight). Note that there was no food cue bias in the fraction of responsive neurons (n = 144 neurons from three mice), nor in response magnitude, and that selectivity was relatively high. NS, not significant (P = 0.4); Kruskal–Wallis test (n = 12, 13, and 12 neurons responding to the food, aversive, and neutral cues, respectively; from three mice). Mean ± s.e.m. Note also the relatively low responsiveness and response magnitude compared with well-trained mice (compare, for example, k below). h–m, To test whether InsCtx food cue bias was dependent on the actual cue orientation or on overexposure to the food cue during early training (see Methods), we switched cue–outcome associations. First, we trained mice in the usual cue–outcome associations and imaged InsCtx to verify food cue bias. We then switched the food cue and neutral cue, and exposed mice to the same number of repetitions of each cue during re-training. We then imaged InsCtx in the same mice again to test whether there was a bias to the new food cue (that is, to the visual grating orientation that now predicted food but that formerly predicted no outcome). h, Schematic of cue–outcome associations in the initial training and then after the food-cue-neutral cue switch. Importantly, in all subsequent analyses, we refer to the Ensure-rewarded cue as the ‘food cue’ regardless of its actual visual grating orientation. i, Experimental protocol of switching cue–outcome associations. j, Fraction of responsive neurons to each cue before and after the food-cue-neutral cue switch. Left: fraction of all imaged neurons before and after (n = 200 pre, n = 232 post; from two mice). Note that after the switch, the overall fraction of responsive neurons to all cues increased, potentially because of increased GCaMP6f expression over time. Right: fraction of all imaged neurons before and after, normalized to the fraction of food-cue-responsive neurons to demonstrate a similar relative level of food cue bias. k, Average response magnitude before and after the food-cue-neutral cue switch. Note the food cue response bias both before and after the switch. *P = 0.01 (pre); **P = 1.4 × 10−5 (post); Kruskal–Wallis test. Pairwise comparisons were as follows. Pre, food cue versus aversive cue, P = 0.01; food cue versus neutral cue, P = 0.02; aversive cue versus neutral cue, P = 0.9; Mann–Whitney U-test (n = 32, 16, and 14 neurons responding to the food, aversive, and neutral cues, respectively; from two mice). Post: food cue versus aversive cue, P = 2.9 × 10−4; food cue versus neutral cue, P = 8.9 × 10−5; aversive cue versus neutral cue, P = 0.2; Mann–Whitney U-test (n = 64, 36, and 21 neurons responding to the food, aversive, and neutral cues, respectively; from two mice). Mean ± s.e.m. l, Cue response selectivity of cue-responsive neurons before and after the food-cue-neutral cue switch. Circles represent number of neurons responsive to a given visual cue. Note the higher cue selectivity of food-cue-responsive neurons both before and after the food-cue-neutral cue switch. m, Behavioural performance of the visual discrimination task before and after switching cue–outcome associations (n = 2 mice). Each dot is data from a single mouse, and lines connect the same mice before and after the switch.
Extended Data Figure 4 InsCtx food cue bias and hunger modulation cannot be explained by licking or other orofacial movements.
a, b, Analysis of cue responses before licking onset. All cue response analyses presented in all figures were performed by analysing data up to 100 ms before the first lick on each trial (see Methods). Here we increased this period to 200 ms and to 300 ms, and demonstrated that food cue bias and hunger modulation persisted and were thus independent of licking. a, Analysis of the fraction of cue-responsive neurons for response epochs from cue onset to either 100 ms, 200 ms, or 300 ms before onset of the first lick. Note a similar food cue bias across analyses. b, Average response magnitude across hunger and satiety for the response epochs described above. Note the response bias to the food cue decreased slightly for analyses restricted to 200 ms and 300 ms before the first lick, but was still prominent and was consistently abolished after satiation. One hundred milliseconds: **P = 2.8 × 10−12 (Hungry); NS, not significant (P = 0.6, Sated); Kruskal–Wallis test. Pairwise comparisons (Hungry): food cue versus aversive cue, P = 1.7 × 10−8; food cue versus neutral cue, P = 7.3 × 10−11; aversive cue versus neutral cue, P = 0.01. Pairwise comparisons (Hungry versus Sated): ***P ≤ 2.7 × 10−19 for all three cues; Mann–Whitney U-test (n = 274, 95, and 85 neurons responding to the food, aversive, and neutral cues, respectively; from six mice). Two hundred milliseconds: **P = 1.5 × 10−8 (Hungry); NS, not significant (P = 0.5, Sated); Kruskal–Wallis test. Pairwise comparisons (Hungry): food cue versus aversive cue, P = 1.8 × 10−4; food cue versus neutral cue, P = 6.7 × 10−8; aversive cue versus neutral cue, P = 0.04. Pairwise comparisons (Hungry versus Sated): ***P ≤ 1.9 × 10−17 for all three cues; Mann–Whitney U-test (n = 274, 95, and 85 neurons responding to the food, aversive, and neutral cues, respectively; from six mice). Three hundred milliseconds: **P = 0.0003 (Hungry); *P = 0.01 (Sated); Kruskal–Wallis test. Pairwise comparisons (Hungry): food cue versus aversive cue, P = 0.04; food cue versus neutral cue: P = 1.7 × 10−4; aversive cue versus neutral cue, P = 0.03. Pairwise comparisons (Hungry versus Sated): ***P ≤ 1.7 × 10−17 for all three cues; Mann–Whitney U-test (n = 274, 95, and 85 neurons responding to the food, aversive, and neutral cues, respectively; from six mice). Mean ± s.e.m. c–g, Analysis of orofacial movements and their potential effect on InsCtx food cue response bias. c, Image of the face of a mouse performing the behavioural task during imaging of InsCtx. Blue rectangle marks the region of interest analysed for orofacial movements (see Methods). d, Orofacial movements in four mice. Left: average across food cue trials of all orofacial movements. Right: average across food cue trials of orofacial movements up to 100 ms before the first lick in each trial (same procedure used to analyse licking-independent cue responses in InsCtx, see a and b above). Only mouse 1 and mouse 3 had significant licking-independent orofacial movements. Mean ± s.e.m. e, Examples of simultaneous recordings of orofacial movements (left) and activity of two neurons (middle and right) from mouse 3. Top: heatmaps of neuronal activity and orofacial movements. Note that for every trial, data were analysed up to 100 ms before the first lick, and all subsequent data points (that is, not analysed for cue responses) are coloured in grey. Bottom: average across trials, mean ± s.e.m. f, Correlation between orofacial movements and single-trial neuronal responses for the two neurons in e. Note that responses in neuron 1 were positively correlated with orofacial movements, while responses in neuron 2 were not correlated with orofacial movements. Orofacial responses have been shown to drive some InsCtx neurons. If food cue responses were caused by orofacial responses, this would be reflected by a positive correlation between neuronal responses and orofacial movements. However, only 3% of food-cue-responsive neurons were positively correlated with orofacial movements across trials (n = 94, from four mice; different mice from those in Fig. 2). This suggests that with our analyses (that is, analysing activity up to 100 ms before the first lick at each trial), orofacial movements do not contribute to food cue responses or to the food cue bias in InsCtx (see g). g, Food cue bias in InsCtx neurons from this separate cohort of four mice, most of which lacked a positive correlation between food cue responses and orofacial movements. Left: fraction of neurons responsive to each cue, from all experiments in which neurons were imaged during simultaneous tracking of orofacial movements. Note the food cue bias. Right: average response magnitude from neurons imaged while also acquiring orofacial movements. Note that the response bias to the food cue is similar to that in Fig. 2. *P = 2.8 × 10−5, Kruskal–Wallis test. Pairwise comparisons: food cue versus aversive cue, P = 0.003; food cue versus neutral cue: P = 5.9 × 10−5; aversive cue versus neutral cue, P = 0.45; Mann–Whitney U-test (n = 94, 23, and 24 neurons responding to the food, aversive, and neutral cues, respectively; from four mice). Mean ± s.e.m.
Extended Data Figure 5 A subset of InsCtx neurons respond to visual cues during satiety; using pre-cue pupil diameter to match trials for similar arousal levels across states.
a, Heatmap of the average neuronal responses to the three visual cues during hunger and satiety for all neurons that were significantly cue-responsive during satiety. b, Fraction of neurons significantly responsive to each cue during satiety (out of 941 recorded neurons from six mice). c, Response magnitude across hunger and satiety in neurons with significant cue responses in the sated state. Note the absence of a significant response bias to the food cue during hunger or satiety in this set of neurons. NS, not significant; P = 0.09 (Hungry); P = 0.59 (Sated); Kruskal–Wallis test (n = 166 neurons from six mice). Pairwise comparisons (Hungry versus Sated): **P ≤ 0.002 for all three cues; Mann–Whitney U-test (n = 274 neurons from six mice). Mean ± s.e.m. d, Selectivity of cue-responsive neurons. Each circle represents all neurons responding to a specific visual cue. Note that neurons responding to any one of the three cues displayed similar response selectivity. e, Pupil diameter dynamics during the discrimination task across hunger (top) and satiety (bottom). Images: examples of a dilated (top) and constricted (bottom) pupil. Grey and purple bars: pre-cue and cue periods, respectively. Same plot shown in Fig. 2. f, Pre-cue pupil diameters across single hungry and sated trials (same mouse from e), demonstrating the procedure for matching trials for similar pre-cue pupil diameters across states (see Methods). Line of unity, dashed pink.
Extended Data Figure 6 Persistent AgRP neuron food cue responses during performance of the visual discrimination task.
a, AgRP neuron fibre photometry. Left: schematic of the fibre photometry approach. Right: image of GCaMP6s-expressing AgRP neurons in the ARC, with the fibre track above. Scale bar, 200 μm. b, Example photometry signal from one food-restricted mouse (same mouse in a) in response to delivery of two small food pellets (0.2 g each). Note an acute decrease in AgRP activity in response to the first pellet, and a subsequent, additional acute decrease after the second pellet, demonstrating that AgRP activity was not completely inhibited after delivery of the first pellet. c, Average cue responses in the visual discrimination task during hunger and satiety, from the same mouse as in b. Note that in the hungry state, AgRP neurons responded to the food cue, but not to the other cues. During the sated state, AgRP neurons were not responsive to any cue. Values are mean ± s.e.m. across 50–55 trials per cue. Shaded region: cue duration. d, AgRP neuron responses to food cues either followed (right) or not followed (left) by another food cue (cues were presented with an interval of 8–10 s, see Methods). Note that AgRP neuron activity returned to baseline levels after each food cue that was not followed by another food cue (but it was followed by either another cue that did not elicit a response or by a blank trial, see Methods). Furthermore, AgRP neurons responded similarly to another presentation of a food cue, spaced 8–10 s apart. Values are mean ± s.e.m. across three mice. Shaded region: food cue duration. e, Heatmaps of food cue responses (same mouse in a–c) across single trials, sorted chronologically (left) or by licking latency (right). Grey and white tick marks show licking onset and Ensure delivery, respectively. Note that there was substantial trial-to-trial variability in food cue responses that could not be explained by trial recency or by licking behaviour. f, Analysis of responses to each of the first 40 presentations of the food cue within a session, averaged across one session from each of three mice. Cue responses were normalized by their mean across the session. These normalized cue responses were then averaged across mice. Dashed red line, normalized response to the first cue presentation. Note that cue responses did not show substantial differences between early and later trials. Values are mean ± s.e.m. g, Schematic of AgRP neuron activity during satiety, the visual discrimination task, and subsequent satiation, on the basis of the experiments in a–f. AgRP neuron activity remained high (relative to satiety) in food-restricted mice during the visual discrimination task, and dropped only transiently after every food cue and consumption of a very small amount of Ensure (5 μl). Satiation (via consumption of either Ensure or chow) reduced the activity of AgRP neurons to lower levels and eliminated subsequent neuronal responses to food cue presentation. Black, dark grey, and light grey arrows: food cue, aversive cue, and neutral cue.
Extended Data Figure 7 Satiation by consumption of Ensure can be labile; InsCtx neuronal responses to the three visual cues during Sated-2 + AgRP and Sated-2; saline injections do not mimic hunger in InsCtx; comparison of InsCtx responses in hungry mice across two daily sessions; time course of decoding accuracy and the necessity of neuronal identity for decoding accuracy.
A, Example licking raster plots from a single session, for food cue trials presented during hunger and satiety. Intermingled aversive and neutral cue presentations are omitted for clarity. Note cue-evoked licking during hunger but not during satiety. B, Examples licking raster plots from two mice, for a session in which each mouse was not fully sated and thus partly re-engaged in the task for a substantial number of trials after initial satiation. Note that in such cases we stopped the experiment, and re-sated the mice with delivery of additional Ensure until they were fully sated (operationally defined as lack of voluntary licking for Ensure, see Methods). C, Heatmap of the average response to the three visual cues during Sated-2 + AgRP and Sated-2 for each neuron (one row per neuron) that was significantly responsive to at least one cue during Sated-2 + AgRP. Vertical dashed lines: visual cue onset. Horizontal dashed lines: grouping of neurons by the cue that evoked the strongest response. D, Saline injections did not mimic hunger in InsCtx. Average response magnitude of InsCtx neurons across hunger, satiety, and saline injections (n = 109 neurons from two mice, different mice from those in c). *P = 0.03 (Hungry); NS, not significant (P ≥ 0.5, Sated-1, Sated-2, Sated-2 + saline injections); Kruskal–Wallis test. Pairwise comparisons across states (Hungry-1 versus Sated-1): food cue, P = 2.4 × 10−5; aversive cue, P = 0.02; neutral cue, P = 1.7 × 10−5. Pairwise comparisons across states (Sated-2 versus Sated-2 + saline injections): P ≥ 0.15 for all three cues; **P < 0.02; NS, not significant; Mann–Whitney U-test. Mean ± s.e.m. Note the population response bias to the food cue in Hungry-1, but not in Sated-2 + saline injections. Furthermore, while there was a food cue bias in the fraction of responsive neurons per cue during hunger (Hungry-1; food cue, 0.16; aversive cue, 0.10; neutral cue, 0.12), saline injections in sated mice did not induce a similar food cue bias (‘Sated-2 + saline inj.’; food cue, 0.05; aversive cue, 0.07; neutral cue, 0.05; n = 109 neurons from two mice). E, Heatmap of all food-cue-responsive neurons during two consecutive imaging sessions, both performed while the mice (n = 4) were hungry. Note that some neurons were similarly responsive on both days, while others were not. F, Average decoding accuracy. The decoder was trained on data from entire populations of neurons recorded during the first ‘Hungry’ session, and tested on other ‘Hungry’ data from the same neurons on the same day, or from the same neurons recorded on the following day (‘Hungry-next-day’ data; n = 4 mice). Note that decoding accuracy was similar. G, Average time course of accuracy of population decoding of whether a food cue was presented versus other cues (see text for details). *All time points that were significantly different from chance (33%) for ‘Sated-2 + AgRP stim.’ (P ≤ 0.03, paired t-test, Holm–Bonferroni correction for multiple comparisons, n = 4 mice). Decoding was performed on single-trial responses of simultaneously imaged ensembles (90–98 neurons per mouse, n = 4 mice). H, a, Overall food cue decoding accuracy after shuffling of neuronal identity. Data are averages of 100 shuffles for each mouse separately (n = 4 mice). Note that shuffling neuronal identity decreased overall decoding accuracy to chance levels. All values are mean ± s.e.m. across mice. H, b, Average time course of accuracy of decoding the food cue versus other cues, after shuffling of neuronal identity (see text for details). Data are averages of 100 shuffles, performed separately for each mouse (n = 4 mice). Note that shuffling neuronal identity decreased decoding accuracy to chance levels, and that decoding accuracy did not increase with time. Thus, decoding accuracy stemmed from the specific pattern of responses across the population and not from global biases in response strength or response type.
a, BLA→InsCtx neurons send axon collaterals to several other BLA targets. Top middle: rabies-based axon collateral mapping approach. Top left: image of the BLA (AAV-FLEX-TVA–mCherry injection site in Emx1-ires-Cre mice), containing neurons labelled with TVA–mCherry (red) and rabies (green). Top right: image of InsCtx (rabies injection site) containing axons labelled with rabies (green). Bottom: images of additional sites that also contained axons labelled with rabies after rabies injection into InsCtx. Scale bars, 200 μm. mPFC, medial prefrontal cortex. b–d, Additional inputs to BLA→InsCtx. b, Schematic of rabies-based, projection-specific monosynaptic tracing of inputs to BLA→InsCtx neurons. c, Images of visual areas containing rabies-labelled neurons (green). Scale bars, 200 μm. d, Images of additional sites containing rabies-labelled neurons (green) that anatomically co-localized with AgRP axons (magenta). vlPAG, ventrolateral periaqueductal grey; aBNST and pBNST, anterior and posterior bed nucleus of the stria terminalis, respectively. e, PVT neurons provide input to BLA→InsCtx neurons and to BLA inhibitory interneurons. Left: schematic of CRACM from PVT neurons to BLA→InsCtx neurons (labelled by CTB injection in InsCtx) or to BLA inhibitory interneurons (labelled by AAV-FLEX–mCherry injection into BLA of Vgat-ires-Cre mice). Note that all recorded neurons received input from PVT (ten out of ten BLA→InsCtx neurons, eight out of eight BLA inhibitory interneurons). Top right: light-evoked excitatory postsynaptic currents (−70 mV holding potential) and inhibitory postsynaptic currents (0 mV holding potential) in a BLA→InsCtx neuron. Note the longer latency and higher temporal jitter of the inhibitory postsynaptic currents, suggesting that they were di/polysynaptic. Bottom right: light-evoked excitatory postsynaptic currents in a BLA interneuron. Note that light-evoked currents had two peaks (monosynaptic and di/polysynaptic), and that the second peak was eliminated by bath application of tetrodotoxin (TTX) and 4-aminopyridine (4-AP), demonstrating that it was di/polysynaptic. Scale bars, 100 pA, 50 ms. f, BNST→BLA neurons did not receive monosynaptic input from AgRP neurons. Top: schematic of CRACM from AgRP neurons to BNST→BLA neurons. Bottom: example recording of AgRP inputs to a BNST→BLA neuron. Black lines are individual sweeps; red line is the average of 15 sweeps. No recorded BNST→BLA neuron received input from AgRP neurons (0 out of 12 connected).
Extended Data Figure 9 PVT→BLA and PVT→NAc/BNST collateral mapping; further behavioural analyses of PVT→BLA excitation.
A–C, PVT→BLA and PVT→NAc/BNST collateral mapping. A, a, Summary of the proposed pathway from AgRP neurons to InsCtx, raising the question of whether PVT→BLA neurons also project to other sites. Scale bars, 200 μm. A, b, PVT→BLA neurons also project to NAc/BNST, but not to any other sites. Top right: rabies-based axon collateral mapping approach. Top left: image of the PVT (TVA–mCherry injection site) containing neurons labelled with TVA–mCherry (red) and rabies (green). Bottom: images of sites examined for the presence of labelled axons. Note that labelled axons were found in BLA (rabies injection site) and NAc/BNST, but not in CeA or dorsomedial hypothalamus (DMH). Scale bars, 200 μm. B, PVT→NAc/BNST neurons sent collaterals to all PVT projection sites examined. Top right: rabies-based axon collateral mapping approach. Top left: image of the PVT (TVA–mCherry injection site) containing neurons labelled with TVA–mCherry (red) and rabies (green). Bottom: images of sites examined for the presence of labelled axons. Note that labelled axons were found in all sites examined. C, Summary of experiments in A and B. PVT→NAc/BNST projected to all PVT projection sites examined, while PVT→BLA only projected to BLA and NAc/BNST, but not to other sites. AStr, amygdalo-striatal transition area; VMH, ventromedial hypothalamus; SCN: suprachiasmatic nucleus. D, Further behavioural analyses of PVT→BLA activation. Fraction of false alarms (left), lick rate (middle), and lick latency (right). Each dot represents one mouse; lines connect two same-day blocks from the same mouse (two saline blocks on day 1: Saline-1.1, Saline-1.2; and a saline block followed by a CNO block on day 2: Saline-2.1, CNO-2.2). Notice no significant changes on either day. Diamonds and error bars, mean ± s.e.m.; NS, not significant (P ≥ 0.2); n = 6 mice.
a, InsCtx food cue modulation index (in the absence versus presence of BLA→InsCtx inhibition) for mice with ipsilateral (left) and bilateral (right) BLA hits. Note overall stability on day 1 (saline versus saline) but attenuation of cue responses (resulting in a negative food cue modulation index) after CNO injection on day 2 in both groups. Values are median ± s.e. median. *P < 0.01; **P < 0.001; Mann–Whitney U-test (ipsilateral: n = 32 neurons from two mice; bilateral: n = 66 neurons from two other mice). b, Behavioural responses to the food cue (left) and to the other cues (right) during BLA→InsCtx inhibition. Each dot represents one mouse; lines connect two same-day blocks from the same mouse (two saline blocks on day 1: Saline-1.1, Saline-1.2; and a saline block followed by a CNO block on day 2: Saline-2.1, CNO-2.2). Dark dots: mice with ipsilateral (to InsCtx imaging hemisphere) hits; light dots: mice with bilateral hits. Note that behavioural responses to the food cue were reduced after BLA→InsCtx inhibition only in the two mice with bilateral hits, but that false alarm rates were reduced in all mice. *P < 0.01; NS, not significant (P > 0.2); paired t-test (n = 4 mice). c, InsCtx food cue modulation index calculated using only those trials with correct behavioural responses. Note overall stability on day 1 but attenuation of cue responses after CNO injection on day 2. Values are median ± s.e. median. *P < 0.03, Mann–Whitney U-test (n = 98 neurons from four mice). d, Lick rate (left) and lick latency (right) during BLA→InsCtx inhibition. Each dot represents one mouse; lines connect two same-day blocks from the same mouse (two saline blocks on day 1: Saline-1.1, Saline-1.2; and a saline block followed by a CNO block on day 2: Saline-2.1, CNO-2.2). Note that lick rates exhibited a small but significant decrease between Saline-1.1 and Saline-1.2 (*P = 0.04), but no significant change between Saline-2.1 and CNO-2.2 (P = 0.3). There was no significant change in lick latency (P > 0.3). NS, not significant; paired t-test (n = 4 mice).
About this article
Cite this article
Livneh, Y., Ramesh, R., Burgess, C. et al. Homeostatic circuits selectively gate food cue responses in insular cortex. Nature 546, 611–616 (2017). https://doi.org/10.1038/nature22375
Cellular and Molecular Life Sciences (2020)
Molecular Psychiatry (2020)
Current Opinion in Neurobiology (2020)
Current Opinion in Neurobiology (2020)