Academic Journal
Large-scale brain network interactions differentiate sweet, sour, and salty food cues during visual processing.
| Title: | Large-scale brain network interactions differentiate sweet, sour, and salty food cues during visual processing. |
|---|---|
| Authors: | Lim M; Food Processing Research Group, Food Convergence Research Division, Korea Food Research Institute, Wanju-gun, 55365, Republic of Korea. manyoell@kfri.re.kr.; Korea Food Research Institute, 245, Nongsaengmyeong-ro, Wanju-gun, 55365, Republic of Korea. manyoell@kfri.re.kr., Kim MJ; Aging Research Group, Food Functionality Research Division, Korea Food Research Institute, Wanju-gun, 55365, Republic of Korea. mjkim14@kfri.re.kr.; Department of Food Biotechnology, University of Science & Technology, Daejeon, 34113, Republic of Korea. mjkim14@kfri.re.kr.; Korea Food Research Institute, 245, Nongsaengmyeong-ro, Wanju-gun, 55365, Republic of Korea. mjkim14@kfri.re.kr. |
| Source: | Scientific reports [Sci Rep] 2026 Apr 22; Vol. 16 (1). Date of Electronic Publication: 2026 Apr 22. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: London : Nature Publishing Group, copyright 2011- |
| MeSH Terms: | Brain*/physiology , Taste Perception*/physiology , Visual Perception*/physiology , Nerve Net*/physiology , Taste*/physiology , Cues*, Humans ; Female ; Male ; Electroencephalography ; Adult ; Brain Mapping ; Food ; Young Adult |
| Abstract: | Visual cues play an important role in food selection by providing early information about taste properties before consumption. However, the large-scale neural mechanisms underlying the perception of different tastes from visual food cues remain poorly understood. Using electroencephalography and whole-brain functional connectivity analysis, we conducted an exploratory, hypothesis-generating investigation of large-scale brain network responses to visual food cues associated with three basic taste qualities, including sweet, sour, and salty. We computed functional connectivity across 100 cortical parcels, and examined the distribution of significant connections across seven canonical brain networks. Brain network connectivity differed across taste categories. Sweet food images engaged default mode network connectivity with somatomotor and attention networks. Sour food images showed significant limbic and salience network connectivity, suggesting heightened affective evaluation. Salty food images evoked the most widespread engagement, with significant control network involvement alongside visual, default mode, and attention networks. Our findings suggest that large-scale network interactions vary with the inferred taste profile of visual food cues. This network-level characterization provides exploratory evidence for taste-associated brain-wide communication during visual food processing. |
| Competing Interests: | Declarations. Competing interests: The authors declare no competing interests. |
| References: | Stover, P. J. et al. Neurobiology of eating behavior, nutrition, and health. J. Intern. Med. 294, 582–604. https://doi.org/10.1111/joim.13699 (2023). (PMID: 10.1111/joim.1369937424220) Motoki, K., Spence, C. & Velasco, C. When visual cues influence taste/flavour perception: A systematic review. Food Qual. Prefer. 111, 104996. ARTN 10499610.1016/j.foodqual.2023.104996 (2023). (PMID: 10.1016/j.foodqual.2023.104996) Avery, J. A., Liu, A. G., Ingeholm, J. E., Gotts, S. J. & Martin, A. Viewing images of foods evokes taste quality-specific activity in gustatory insular cortex. Proc. Natl. Acad. Sci. U. S. A. 118, 2010932118. https://doi.org/10.1073/pnas.2010932118 (2021). (PMID: 10.1073/pnas.2010932118) Lim, M., Park, S., Lee, Y. & Kwak, H. S. Brain responses and connectivity to visual meal compositions: An EEG investigation into food liking. Food Qual. Prefer. 112, doi:ARTN 10502910.1016/j.foodqual.105029 (2023). (2023). Moerel, D., Psihoyos, J. & Carlson, T. A. The time-course of food representation in the human brain. J. Neurosci. https://doi.org/10.1523/JNEUROSCI.1101-23.2024 (2024). (PMID: 10.1523/JNEUROSCI.1101-23.20243910705811411586) Spence, C., Okajima, K., Cheok, A. D., Petit, O. & Michel, C. Eating with our eyes: From visual hunger to digital satiation. Brain Cogn. 110, 53–63. https://doi.org/10.1016/j.bandc.2015.08.006 (2016). (PMID: 10.1016/j.bandc.2015.08.00626432045) Simmons, W. K. et al. Category-specific integration of homeostatic signals in caudal but not rostral human insula. Nat. Neurosci. 16, 1551–1552. https://doi.org/10.1038/nn.3535 (2013). (PMID: 10.1038/nn.3535240775653835665) Devoto, F. et al. Hungry brains: A meta-analytical review of brain activation imaging studies on food perception and appetite in obese individuals. Neurosci. Biobehav. Rev. 94, 271–285. https://doi.org/10.1016/j.neubiorev.2018.07.017 (2018). (PMID: 10.1016/j.neubiorev.2018.07.01730071209) Zheng, L., Miao, M. & Gan, Y. A systematic and meta-analytic review on the neural correlates of viewing high- and low-calorie foods among normal-weight adults. Neurosci. Biobehav. Rev. 138, 104721. https://doi.org/10.1016/j.neubiorev.2022.104721 (2022). (PMID: 10.1016/j.neubiorev.2022.10472135667634) Vartanian, M. et al. Neural responses to visual food cues according to weight and hunger state: A systematic review and meta-analysis. Neurosci. Biobehav. Rev. 177, 106301. https://doi.org/10.1016/j.neubiorev.2025.106301 (2025). (PMID: 10.1016/j.neubiorev.2025.10630140730315) Dagher, A. Functional brain imaging of appetite. Trends Endocrinol. Metab. 23, 250–260. https://doi.org/10.1016/j.tem.2012.02.009 (2012). (PMID: 10.1016/j.tem.2012.02.00922483361) Rolls, E. T. The orbitofrontal cortex, food reward, body weight and obesity. Soc. Cogn. Affect. Neurosci. 18, nsab044. https://doi.org/10.1093/scan/nsab044 (2023). (PMID: 10.1093/scan/nsab044338302729997078) Khosla, M., Ratan Murty, N. A. & Kanwisher, N. A highly selective response to food in human visual cortex revealed by hypothesis-free voxel decomposition. Curr. Biol. 32 (e4159), 4159–4171. https://doi.org/10.1016/j.cub.2022.08.009 (2022). (PMID: 10.1016/j.cub.2022.08.009360279109561032) Jain, N. et al. Selectivity for food in human ventral visual cortex. Commun. Biol. 6, 175. https://doi.org/10.1038/s42003-023-04546-2 (2023). (PMID: 10.1038/s42003-023-04546-2367926939932019) Avery, J. A., Carrington, M. & Martin, A. A common neural code for representing imagined and inferred tastes. Prog. Neurobiol. 223, 102423. https://doi.org/10.1016/j.pneurobio.2023.102423 (2023). (PMID: 10.1016/j.pneurobio.2023.1024233680549910040442) Bassett, D. S. & Sporns, O. Network neuroscience. Nat. Neurosci. 20, 353–364. https://doi.org/10.1038/nn.4502 (2017). (PMID: 10.1038/nn.4502282308445485642) van der Laan, L. N., de Ridr, D. T., Viergever, M. A. & Smeets, P. A. The first taste is always with the eyes: A meta-analysis on the neural correlates of processing visual food cues. Neuroimage 55, 296–303. https://doi.org/10.1016/j.neuroimage.2010.11.055 (2011). (PMID: 10.1016/j.neuroimage.2010.11.05521111829) Spence, C. Multisensory flavor perception. Cell 161, 24–35. https://doi.org/10.1016/j.cell.2015.03.007 (2015). (PMID: 10.1016/j.cell.2015.03.00725815982) Foroni, F., Pergola, G., Argiris, G. & Rumiati, R. I. The FoodCast research image database (FRIDa). Front. Hum. Neurosci. 7, 51. https://doi.org/10.3389/fnhum.2013.00051 (2013). (PMID: 10.3389/fnhum.2013.00051234597813585434) Peirce, J. et al. PsychoPy2: Experiments in behavior made easy. Behav. Res. Methods 51, 195–203. https://doi.org/10.3758/s13428-018-01193-y (2019). (PMID: 10.3758/s13428-018-01193-y307342066420413) Delorme, A. & Makeig, S. EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods. 134, 9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009 (2004). (PMID: 10.1016/j.jneumeth.2003.10.00915102499) Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D. & Leahy, R. M. Brainstorm: A user-friendly application for MEG/EEG analysis. Comput. Intell. Neurosci. 2011, 879716. https://doi.org/10.1155/2011/879716 (2011). (PMID: 10.1155/2011/879716215842563090754) Evans, A. C., Janke, A. L., Collins, D. L. & Baillet, S. Brain templates and atlases. Neuroimage 62, 911–922. https://doi.org/10.1016/j.neuroimage.2012.01.024 (2012). (PMID: 10.1016/j.neuroimage.2012.01.02422248580) Gramfort, A., Papadopoulo, T., Olivi, E. & Clerc, M. OpenMEEG: Opensource software for quasistatic bioelectromagnetics. Biomed. Eng. Online. https://doi.org/10.1186/1475-925X-9-45 (2010). (PMID: 10.1186/1475-925X-9-45208192042949879) Dale, A. M. et al. Dynamic statistical parametric mapping: Combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron 26, 55–67. https://doi.org/10.1016/s0896-6273(00)81138-1 (2000). (PMID: 10.1016/s0896-6273(00)81138-110798392) Schaefer, A. et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb. Cortex 28, 3095–3114. https://doi.org/10.1093/cercor/bhx179 (2018). (PMID: 10.1093/cercor/bhx179289816126095216) Yeo, B. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165. https://doi.org/10.1152/jn.00338.2011 (2011). (PMID: 10.1152/jn.00338.2011216537233174820) Brookes, M. J. et al. Measuring functional connectivity using MEG: Methodology and comparison with fcMRI. Neuroimage 56, 1082–1104. https://doi.org/10.1016/j.neuroimage.2011.02.054 (2011). (PMID: 10.1016/j.neuroimage.2011.02.054213529253224862) Hipp, J. F., Hawellek, D. J., Corbetta, M., Siegel, M. & Engel, A. K. Large-scale cortical correlation structure of spontaneous oscillatory activity. Nat. Neurosci. 15, 884–890. https://doi.org/10.1038/nn.3101 (2012). (PMID: 10.1038/nn.3101225614543861400) Zalesky, A., Fornito, A. & Bullmore, E. T. Network-based statistic: Identifying differences in brain networks. Neuroimage 53, 1197–1207. https://doi.org/10.1016/j.neuroimage.2010.06.041 (2010). (PMID: 10.1016/j.neuroimage.2010.06.04120600983) Xia, M., Wang, J. & He, Y. BrainNet viewer: A network visualization tool for human brain connectomics. PLoS One. 8, e68910. https://doi.org/10.1371/journal.pone.0068910 (2013). (PMID: 10.1371/journal.pone.0068910238619513701683) Menon, V. 20 years of the default mode network: A review and synthesis. Neuron 111, 2469–2487. https://doi.org/10.1016/j.neuron.2023.04.023 (2023). (PMID: 10.1016/j.neuron.2023.04.0233716796810524518) Smallwood, J. et al. The default mode network in cognition: A topographical perspective. Nat. Rev. Neurosci. 22, 503–513. https://doi.org/10.1038/s41583-021-00474-4 (2021). (PMID: 10.1038/s41583-021-00474-434226715) Breslin, P. A. An evolutionary perspective on food and human taste. Curr. Biol. 23, R409-418. https://doi.org/10.1016/j.cub.2013.04.010 (2013). (PMID: 10.1016/j.cub.2013.04.010236603643680351) Rudebeck, P. H. & Murray, E. A. The orbitofrontal oracle: Cortical mechanisms for the prediction and evaluation of specific behavioral outcomes. Neuron 84, 1143–1156. https://doi.org/10.1016/j.neuron.2014.10.049 (2014). (PMID: 10.1016/j.neuron.2014.10.049255213764271193) Seeley, W. W. The Salience Network: A Neural System for Perceiving and Responding to Homeostatic Demands. J. Neurosci. 39, 9878–9882. https://doi.org/10.1523/JNEUROSCI.1138-17.2019 (2019). (PMID: 10.1523/JNEUROSCI.1138-17.2019316766046978945) Avery, J. A. Against gustotopic representation in the human brain: There is no Cartesian Restaurant. Curr. Opin. Physiol. 20, 23–28. https://doi.org/10.1016/j.cophys.2021.01.005 (2021). (PMID: 10.1016/j.cophys.2021.01.005335214137839947) Hare, T. A., Camerer, C. F. & Rangel, A. Self-control in decision-making involves modulation of the vmPFC valuation system. Science 324, 646–648. https://doi.org/10.1126/science.1168450 (2009). (PMID: 10.1126/science.116845019407204) Hare, T. A., Malmaud, J. & Rangel, A. Focusing attention on the health aspects of foods changes value signals in vmPFC and improves dietary choice. J. Neurosci. 31, 11077–11087. https://doi.org/10.1523/JNEUROSCI.6383-10.2011 (2011). (PMID: 10.1523/JNEUROSCI.6383-10.2011217955566623079) Avery, J. A. et al. Automatic engagement of limbic and prefrontal networks in response to food images reflects distinct information about food hedonics and inhibitory control. Commun. Biol. 8, 270. https://doi.org/10.1038/s42003-025-07704-w (2025). (PMID: 10.1038/s42003-025-07704-w3997960211842766) Leech, R., Kamourieh, S., Beckmann, C. F. & Sharp, D. J. Fractionating the default mode network: Distinct contributions of the ventral and dorsal posterior cingulate cortex to cognitive control. J. Neurosci. 31, 3217–3224. https://doi.org/10.1523/JNEUROSCI.5626-10.2011 (2011). (PMID: 10.1523/JNEUROSCI.5626-10.2011213680336623935) Margulies, D. S. et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc. Natl. Acad. Sci. U. S. A. 113, 12574–12579. https://doi.org/10.1073/pnas.1608282113 (2016). (PMID: 10.1073/pnas.1608282113277910995098630) Vossel, S., Geng, J. J. & Fink, G. R. Dorsal and ventral attention systems: Distinct neural circuits but collaborative roles. Neuroscientist 20, 150–159. https://doi.org/10.1177/1073858413494269 (2014). (PMID: 10.1177/107385841349426923835449) Kurth, F., Zilles, K., Fox, P. T., Laird, A. R. & Eickhoff, S. B. A link between the systems: Functional differentiation and integration within the human insula revealed by meta-analysis. Brain Struct. Funct. 214, 519–534. https://doi.org/10.1007/s00429-010-0255-z (2010). (PMID: 10.1007/s00429-010-0255-z205123764801482) Menon, V. & Uddin, L. Q. Saliency, switching, attention and control: A network model of insula function. Brain Struct. Funct. 214, 655–667. https://doi.org/10.1007/s00429-010-0262-0 (2010). (PMID: 10.1007/s00429-010-0262-0205123702899886) Sridharan, D., Levitin, D. J. & Menon, V. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc. Natl. Acad. Sci. U. S. A. 105, 12569–12574. https://doi.org/10.1073/pnas.0800005105 (2008). (PMID: 10.1073/pnas.0800005105187236762527952) Corbetta, M. & Shulman, G. L. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3, 201–215. https://doi.org/10.1038/nrn755 (2002). (PMID: 10.1038/nrn75511994752) Barrett, L. F. & Simmons, W. K. Interoceptive predictions in the brain. Nat. Rev. Neurosci. 16, 419–429. https://doi.org/10.1038/nrn3950 (2015). (PMID: 10.1038/nrn3950260167444731102) Coricelli, C., Toepel, U., Notter, M. L., Murray, M. M. & Rumiati, R. I. Distinct brain representations of processed and unprocessed foods. Eur. J. Neurosci. 50, 3389–3401. https://doi.org/10.1111/ejn.14498 (2019). (PMID: 10.1111/ejn.1449831228866) |
| Grant Information: | Main Research Program (E0232201) Korea Food Research Institute |
| Contributed Indexing: | Keywords: Brain network; Electroencephalography; Functional connectivity; Taste; Visual food perception |
| Entry Date(s): | Date Created: 20260422 Date Completed: 20260628 Latest Revision: 20260726 |
| Update Code: | 20260726 |
| PubMed Central ID: | PMC13270053 |
| DOI: | 10.1038/s41598-026-49773-9 |
| PMID: | 42020504 |
| Database: | MEDLINE |
| ISSN: | 2045-2322 |
|---|---|
| DOI: | 10.1038/s41598-026-49773-9 |