Skip to main content Accessibility help

Snack food as a modulator of human resting-state functional connectivity

  • Andrea Mendez-Torrijos (a1), Silke Kreitz (a1), Claudiu Ivan (a1), Laura Konerth (a1), Julie Rösch (a2), Monika Pischetsrieder (a3), Gunther Moll (a4), Oliver Kratz (a4), Arnd Dörfler (a2), Stefanie Horndasch (a4) and Andreas Hess (a1)...



To elucidate the mechanisms of how snack foods may induce non-homeostatic food intake, we used resting state functional magnetic resonance imaging (fMRI), as resting state networks can individually adapt to experience after short time exposures. In addition, we used graph theoretical analysis together with machine learning techniques (support vector machine) to identifying biomarkers that can categorize between high-caloric (potato chips) vs. low-caloric (zucchini) food stimulation.


Seventeen healthy human subjects with body mass index (BMI) 19 to 27 underwent 2 different fMRI sessions where an initial resting state scan was acquired, followed by visual presentation of different images of potato chips and zucchini. There was then a 5-minute pause to ingest food (day 1=potato chips, day 3=zucchini), followed by a second resting state scan. fMRI data were further analyzed using graph theory analysis and support vector machine techniques.


Potato chips vs. zucchini stimulation led to significant connectivity changes. The support vector machine was able to accurately categorize the 2 types of food stimuli with 100% accuracy. Visual, auditory, and somatosensory structures, as well as thalamus, insula, and basal ganglia were found to be important for food classification. After potato chips consumption, the BMI was associated with the path length and degree in nucleus accumbens, middle temporal gyrus, and thalamus.


The results suggest that high vs. low caloric food stimulation in healthy individuals can induce significant changes in resting state networks. These changes can be detected using graph theory measures in conjunction with support vector machine. Additionally, we found that the BMI affects the response of the nucleus accumbens when high caloric food is consumed.


Corresponding author

*Address for correspondence: Prof. Andreas Hess, Institut für Pharmakologie und Toxikologie, Fahrstraße 17, 91054 Erlangen (Deutschland). (Email:


Hide All

These authors contributed equally.

We would like to thank all the participants of the study. We would also like to thank the excellent technical support in the Neuroradiology Department of the FAU, as well as Jutta Prade and Marina Sergeeva from the Institute of Experimental and Clinical Pharmacology and Toxicology.

This project was supported by the Neurotrition Project by FAU Emerging Fields Initiative.



Hide All
1. Pursey, KM, Stanwell, P, Gearhardt, AN, Collins, CE, Burrows, TL. The prevalence of food addiction as assessed by the Yale Food Addiction Scale: a systematic review. Nutrients. 2014; 6(10): 45524590.
2. Small, DM, Zatorre, RJ, Dagher, A, Evans, AC, Jones-Gotman, M. Changes in brain activity related to eating chocolate: from pleasure to aversion. Brain. 2001; 124(Pt 9): 17201733.
3. Tillisch, K, Labus, J, Kilpatrick, L, Jiang, Z, Stains, J, Ebrat, B, et al. Consumption of fermented milk product with probiotic modulates brain activity. Gastroenterology. 2013; 144(7): 13941401, e1391–e1394.
4. Gomez-Pinilla, F. Brain foods: the effects of nutrients on brain function. Nat Rev Neurosci. 2008; 9(7): 568578.
5. Thomas, MA, Ryu, V, Bartness, TJ. Central ghrelin increases food foraging/hoarding that is blocked by GHSR antagonism and attenuates hypothalamic paraventricular nucleus neuronal activation. Am J Physiol Regul Integr Comp Physiol. 2016; 310(3): R275R285.
6. Hoch, T, Pischetsrieder, M, Hess, A. Snack food intake in ad libitum fed rats is triggered by the combination of fat and carbohydrates. Front Psychol. 2014; 5: 250.
7. Hoch, T, Kreitz, S, Gaffling, S, Pischetsrieder, M, Hess, A. Fat/carbohydrate ratio but not energy density determines snack food intake and activates brain reward areas. Sci Rep. 2015; 5: 10041.
8. Sharma, AM, Padwal, R. Obesity is a sign—over-eating is a symptom: an aetiological framework for the assessment and management of obesity. Obes Rev. 2010; 11(5): 362370.
9. Gearhardt, AN, Davis, C, Kuschner, R, Brownell, KD. The addiction potential of hyperpalatable foods. Curr Drug Abuse Rev. 2011; 4(3): 140145.
10. Schulte, EM, Avena, NM, Gearhardt, AN. Which foods may be addictive? The roles of processing, fat content, and glycemic load. PLoS One. 2015; 10(2): e0117959.
11. Burger, KS, Stice, E. Elevated energy intake is correlated with hyperresponsivity in attentional, gustatory, and reward brain regions while anticipating palatable food receipt. Am J Clin Nutr. 2013; 97(6): 11881194.
12. Volkow, ND, Wang, GJ, Telang, F, Fowler, JS, Thanos, PK, Logan, J, et al. Low dopamine striatal D2 receptors are associated with prefrontal metabolism in obese subjects: possible contributing factors. Neuroimage. 2008; 42(4): 15371543.
13. Uher, R, Murphy, T, Brammer, MJ, Dalgleish, T, Phillips, ML, Ng, VW, et al. Medial prefrontal cortex activity associated with symptom provocation in eating disorders. Am J Psychiatry. 2004; 161(7): 12381246.
14. Brooks, SJ, O’Daly, OG, Uher, R, Friederich, HC, Giampietro, V, Brammer, M, et al. Differential neural responses to food images in women with bulimia versus anorexia nervosa. PLoS One. 2011; 6(7): e22259.
15. Frank, GK. Advances from neuroimaging studies in eating disorders. CNS Spectr. 2015; 20(4): 391400.
16. Rolls, ET. Taste, olfactory and food texture reward processing in the brain and the control of appetite. Proc Nutr Soc. 2012; 71(4): 488501.
17. Grabenhorst, F, Rolls, ET. Selective attention to affective value alters how the brain processes taste stimuli. Eur J Neurosci. 2008; 27(3): 723729.
18. Grabenhorst, F, Rolls, ET, Bilderbeck, A. How cognition modulates affective responses to taste and flavor: top-down influences on the orbitofrontal and pregenual cingulate cortices. Cereb Cortex. 2008; 18(7): 15491559.
19. Hare, TA, Camerer, CF, Rangel, A. Self-control in decision-making involves modulation of the vmPFC valuation system. Science. 2009; 324(5927): 646648.
20. Lee, MH, Smyser, CD, Shimony, JS. Resting-state fMRI: a review of methods and clinical applications. AJNR Am J Neuroradiol. 2013; 34(10): 18661872.
21. Damoiseaux, JS, Rombouts, SA, Barkhof, F, Scheltens, P, Stam, CJ, Smith, SM, et al. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A. 2006; 103(37): 1384813853.
22. Ma, N, Liu, Y, Li, N, Wang, CX, Zhang, H, Jiang, XF, et al. Addiction related alteration in resting-state brain connectivity. Neuroimage. 2010; 49(1): 738744.
23. Kelly, C, Zuo, XN, Gotimer, K, Cox, CL, Lynch, L, Brock, D, et al. Reduced interhemispheric resting state functional connectivity in cocaine addiction. Biol Psychiatry. 2011; 69(7): 684692.
24. Liu, Y, Liang, M, Zhou, Y, He, Y, Hao, Y, Song, M, et al. Disrupted small-world networks in schizophrenia. Brain. 2008; 131(Pt 4): 945961.
25. Zhao, X, Liu, Y, Wang, X, Liu, B, Xi, Q, Guo, Q, et al. Disrupted small-world brain networks in moderate Alzheimer’s disease: a resting-state FMRI study. PLoS One. 2012; 7(3): e33540.
26. dos Santos Siqueira, A, Biazoli Junior, CE, Comfort, WE, Rohde, LA, Sato, JR. Abnormal functional resting-state networks in ADHD: graph theory and pattern recognition analysis of fMRI data. BioMed Research International. 2014; 2014: 380531.
27. Khazaee, A, Ebrahimzadeh, A, Babajani-Feremi, A. Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory. Clin Neurophysiol. 2015; 126(11): 21322141.
28. van den Heuvel, MP, Mandl, RC, Stam, CJ, Kahn, RS, Hulshoff Pol, HE. Aberrant frontal and temporal complex network structure in schizophrenia: a graph theoretical analysis. J Neurosci. 2010; 30(47): 1591515926.
29. Bullmore, E, Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009; 10(3): 186198.
30. Cortes, C, Vapnik, V. Support-vector networks. Machine Learning. 1995; 20(3): 273297.
31. Quanquan, G, Zhenhui, L, Jiawei, H. Generalized Fisher score for feature selection. Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence; 2012; Barcelona, Spain.
32. Dosenbach, NU, Nardos, B, Cohen, AL, Fair, DA, Power, JD, Church, JA, et al. Prediction of individual brain maturity using fMRI. Science. 2010; 329(5997): 13581361.
33. Sacchet, MD, Prasad, G, Foland-Ross, LC, Thompson, PM, Gotlib, IH. Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory. Front Psychiatry. 2015; 6: 21.
34. Li, Y, Qin, Y, Chen, X, Li, W. Exploring the functional brain network of Alzheimer’s disease: based on the computational experiment. PLoS One. 2013; 8(9): e73186.
35. Malik, S, McGlone, F, Dagher, A. State of expectancy modulates the neural response to visual food stimuli in humans. Appetite. 2011; 56(2): 302309.
36. Vaughan, JT, Garwood, M, Collins, CM, Liu, W, DelaBarre, L, Adriany, G, et al. 7T vs. 4T: RF power, homogeneity, and signal-to-noise comparison in head images. Magn Reson Med. 2001; 46(1): 2430.
37. Goebel, R, Esposito, F, Formisano, E. Analysis of functional image analysis contest (FIAC) data with brainvoyager QX: from single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis. Hum Brain Mapp. 2006; 27(5): 392401.
38. Kreitz, S, de Celis Alonso, B, Uder, M, Hess, A. A new analysis of resting state connectivity and graph theory reveals distinctive short-term modulations due to whisker stimulation in rats. bioRxiv. Preprint. DOI: 10.1101/223057.
39. Watts, DJ, Strogatz, SH. Collective dynamics of ‘small-world’ networks. Nature. 1998; 393(6684): 440442.
40. Rubinov, M, Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010; 52(3): 10591069.
41. Flake, GW, Lawrence, S, Giles, CL, Coetzee, FM. Self-organization and identification of Web communities. Computer. 2002; 35(3): 6670.
42. Kleinberg, JM. Authoritative sources in a hyperlinked environment. Journal of the ACM. 1999; 46(5): 604632.
43. Zalesky, A, Fornito, A, Bullmore, ET. Network-based statistic: identifying differences in brain networks. Neuroimage. 2010; 53(4): 11971207.
44. Braun, U, Plichta, MM, Esslinger, C, Sauer, C, Haddad, L, Grimm, O, et al. Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures. Neuroimage. 2012; 59(2): 14041412.
45. Chen, S, Ross, TJ, Zhan, W, Myers, CS, Chuang, KS, Heishman, SJ, et al. Group independent component analysis reveals consistent resting-state networks across multiple sessions. Brain Res. 2008; 1239: 141151.
46. Smith, SM, Beckmann, CF, Ramnani, N, Woolrich, MW, Bannister, PR, Jenkinson, M, et al. Variability in fMRI: a re-examination of inter-session differences. Hum Brain Mapp. 2005; 24(3): 248257.
47. Bellman, RE. Dynamic Programming. Mineola, New York. Dover Publications; 2003.
48. He, X, Cai, D, Niyogi, P. Laplacian score for feature selection. Proceedings of the 18th International Conference on Neural Information Processing Systems; 2005; Vancouver, British Columbia, Canada.
49. Chang, Y-W, Lin, C-J. Feature Ranking Using Linear SVM. Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008; 2008; Proceedings of Machine Learning Research.
50. Cawley, GC, Talbot, NLC. Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers. Pattern Recognition. 2003; 36(11): 25852592.
51. Hastie, T, Tibshirani, R, Friedman, J. The Elements of Statistical Learning. Vol. 2. New York: Springer; 2009.
52. von Elm, E, Altman, DG, Egger, M, Pocock, SJ, Gotzsche, PC, Vandenbroucke, JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. Ann Intern Med. 2007; 147(8): 573577.
53. Sanz-Arigita, EJ, Schoonheim, MM, Damoiseaux, JS, Rombouts, SA, Maris, E, Barkhof, F, et al. Loss of ‘small-world’ networks in Alzheimer’s disease: graph analysis of FMRI resting-state functional connectivity. PLoS One. 2010; 5(11): e13788.
54. Zhou, Y, Lui, YW. Small-world properties in mild cognitive impairment and early Alzheimer’s disease: a cortical thickness MRI study. ISRN Geriatr. 2013; 2013: 542080.
55. Zald, DH. Orbitofrontal cortex contributions to food selection and decision making. Ann Behav Med. 2009; 38(Suppl 1): S18S24.
56. Killgore, WD, Young, AD, Femia, LA, Bogorodzki, P, Rogowska, J, Yurgelun-Todd, DA. Cortical and limbic activation during viewing of high- versus low-calorie foods. Neuroimage. 2003; 19(4): 13811394.
57. Nederkoorn, C, Smulders, FT, Jansen, A. Cephalic phase responses, craving and food intake in normal subjects. Appetite. 2000; 35(1): 4555.
58. Rolls, ET. The orbitofrontal cortex and reward. Cereb Cortex. 2000; 10(3): 284294.
59. Staresina, BP, Davachi, L. Differential encoding mechanisms for subsequent associative recognition and free recall. J Neurosci. 2006; 26(36): 91629172.
60. Bookheimer, S. Functional MRI of language: new approaches to understanding the cortical organization of semantic processing. Annu Rev Neurosci. 2002; 25: 151188.
61. Vandenberghe, R, Price, C, Wise, R, Josephs, O, Frackowiak, RSJ. Functional anatomy of a common semantic system for words and pictures. Nature. 1996; 383(6597): 254256.
62. Zatorre, RJ, Belin, P, Penhune, VB. Structure and function of auditory cortex: music and speech. Trends Cogn Sci. 2002; 6(1): 3746.
63. von Saldern, S, Noppeney, U. Sensory and striatal areas integrate auditory and visual signals into behavioral benefits during motion discrimination. J Neurosci. 2013; 33(20): 88418849.
64. van der Laan, LN, de Ridder, DT, Viergever, MA, Smeets, PA. The first taste is always with the eyes: a meta-analysis on the neural correlates of processing visual food cues. Neuroimage. 2011; 55(1): 296303.
65. Killgore, WD, Yurgelun-Todd, DA. Positive affect modulates activity in the visual cortex to images of high calorie foods. Int J Neurosci. 2007; 117(5): 643653.
66. Simmons, WK, Martin, A, Barsalou, LW. Pictures of appetizing foods activate gustatory cortices for taste and reward. Cereb Cortex. 2005; 15(10): 16021608.
67. De Araujo, IE, Rolls, ET. Representation in the human brain of food texture and oral fat. J Neurosci. 2004; 24(12): 30863093.
68. Frank, GK, Reynolds, JR, Shott, ME, Jappe, L, Yang, TT, Tregellas, JR, et al. Anorexia nervosa and obesity are associated with opposite brain reward response. Neuropsychopharmacology. 2012; 37(9): 20312046.
69. Piech, RM, Lewis, J, Parkinson, CH, Owen, AM, Roberts, AC, Downing, PE, et al. Neural correlates of appetite and hunger-related evaluative judgments. PLoS One. 2009; 4(8): e6581.
70. Thomas, JM, Higgs, S, Dourish, CT, Hansen, PC, Harmer, CJ, McCabe, C. Satiation attenuates BOLD activity in brain regions involved in reward and increases activity in dorsolateral prefrontal cortex: an fMRI study in healthy volunteers. Am J Clin Nutr. 2015; 101(4): 697704.
71. Siep, N, Roefs, A, Roebroeck, A, Havermans, R, Bonte, ML, Jansen, A. Hunger is the best spice: an fMRI study of the effects of attention, hunger and calorie content on food reward processing in the amygdala and orbitofrontal cortex. Behav Brain Res. 2009; 198(1): 149158.
72. Mehta, S, Melhorn, SJ, Smeraglio, A, Tyagi, V, Grabowski, T, Schwartz, MW, et al. Regional brain response to visual food cues is a marker of satiety that predicts food choice. Am J Clin Nutr. 2012; 96(5): 989999.
73. Kelley, AE, Baldo, BA, Pratt, WE, Will, MJ. Corticostriatal-hypothalamic circuitry and food motivation: integration of energy, action and reward. Physiol Behav. 2005; 86(5): 773795.
74. Demos, KE, Heatherton, TF, Kelley, WM. Individual differences in nucleus accumbens activity to food and sexual images predict weight gain and sexual behavior. J Neurosci. 2012; 32(16): 55495552.
75. Coveleskie, K, Gupta, A, Kilpatrick, LA, Mayer, ED, Ashe-McNalley, C, Stains, J, et al. Altered functional connectivity within the central reward network in overweight and obese women. Nutr Diabetes. 2015; 5(1): e148.
76. Wang, GJ, Tomasi, D, Convit, A, Logan, J, Wong, CT, Shumay, E, et al. BMI modulates calorie-dependent dopamine changes in accumbens from glucose intake. PLoS One. 2014; 9(7): e101585.
77. Smeets, PA, de Graaf, C, Stafleu, A, van Osch, MJ, Nievelstein, RA, van der Grond, J. Effect of satiety on brain activation during chocolate tasting in men and women. Am J Clin Nutr. 2006; 83(6): 12971305.
78. Frank, S, Laharnar, N, Kullmann, S, Veit, R, Canova, C, Hegner, YL, et al. Processing of food pictures: influence of hunger, gender and calorie content. Brain Res. 2010; 1350: 159166.
79. Button, KS, Ioannidis, JP, Mokrysz, C, Nosek, BA, Flint, J, Robinson, ES, et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci. 2013; 14(5): 365376.
80. Kullmann, S, Heni, M, Linder, K, Zipfel, S, Haring, HU, Veit, R, et al. Resting-state functional connectivity of the human hypothalamus. Hum Brain Mapp. 2014; 35(12): 60886096.
81. Grill, HJ, Skibicka, KP, Hayes, MR. Imaging obesity: fMRI, food reward, and feeding. Cell Metab. 2007; 6(6): 423425.
82. Cornier, MA, Von Kaenel, SS, Bessesen, DH, Tregellas, JR. Effects of overfeeding on the neuronal response to visual food cues. Am J Clin Nutr. 2007; 86(4): 965971.



Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed