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

Published online by Cambridge University Press:  04 April 2018

Andrea Mendez-Torrijos ,
Silke Kreitz ,
Claudiu Ivan ,
Laura Konerth ,
Julie Rösch ,
Monika Pischetsrieder ,
Gunther Moll ,
Oliver Kratz ,
Arnd Dörfler  and
Stefanie Horndasch
...Show all authors
Andrea Mendez-Torrijos
Affiliation:
Institute of Experimental and Clinical Pharmacology and Toxicology, Emil Fischer Center, University of Erlangen-Nuremberg, Erlangen, Germany
Silke Kreitz
Affiliation:
Institute of Experimental and Clinical Pharmacology and Toxicology, Emil Fischer Center, University of Erlangen-Nuremberg, Erlangen, Germany
Claudiu Ivan
Affiliation:
Institute of Experimental and Clinical Pharmacology and Toxicology, Emil Fischer Center, University of Erlangen-Nuremberg, Erlangen, Germany
Laura Konerth
Affiliation:
Institute of Experimental and Clinical Pharmacology and Toxicology, Emil Fischer Center, University of Erlangen-Nuremberg, Erlangen, Germany
Julie Rösch
Affiliation:
Department of Neuroradiology, University of Erlangen-Nuremberg, Erlangen, Germany
Monika Pischetsrieder
Affiliation:
Department of Chemistry and Pharmacy, Food Chemistry Division, Emil Fischer Center, University of Erlangen-Nuremberg, Erlangen, Germany
Gunther Moll
Affiliation:
Department of Child and Adolescent Mental Health, University Hospital of Erlangen, Erlangen, Germany
Oliver Kratz
Affiliation:
Department of Child and Adolescent Mental Health, University Hospital of Erlangen, Erlangen, Germany
Arnd Dörfler
Affiliation:
Department of Neuroradiology, University of Erlangen-Nuremberg, Erlangen, Germany
Stefanie Horndasch
Affiliation:
Department of Child and Adolescent Mental Health, University Hospital of Erlangen, Erlangen, Germany
Andreas Hess*
Affiliation:
Institute of Experimental and Clinical Pharmacology and Toxicology, Emil Fischer Center, University of Erlangen-Nuremberg, Erlangen, Germany
*
*Address for correspondence: Prof. Andreas Hess, Institut für Pharmakologie und Toxikologie, Fahrstraße 17, 91054 Erlangen (Deutschland). (Email: Andreas.Hess@fau.de)
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Abstract

Objective

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.

Methods

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.

Results

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.

Conclusion

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.

Keywords

Food intakegraph theoryresting state networksRS-fMRIsnack foodsupport vector machine
Type
Original Research
Information
CNS Spectrums , Volume 23 , Special Issue 5: Theme: Obsessive-Compulsive Disorder , October 2018 , pp. 321 - 332
Copyright
© Cambridge University Press 2018 

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Footnotes

†

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.

References

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): 4552–4590.Google Scholar
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): 1720–1733.Google Scholar
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): 1394–1401, e1391–e1394.Google Scholar
4. Gomez-Pinilla, F. Brain foods: the effects of nutrients on brain function. Nat Rev Neurosci. 2008; 9(7): 568–578.Google Scholar
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): R275–R285.Google Scholar
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.Google Scholar
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.Google Scholar
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): 362–370.Google Scholar
9. Gearhardt, AN, Davis, C, Kuschner, R, Brownell, KD. The addiction potential of hyperpalatable foods. Curr Drug Abuse Rev. 2011; 4(3): 140–145.Google Scholar
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.Google Scholar
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): 1188–1194.Google Scholar
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): 1537–1543.Google Scholar
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): 1238–1246.Google Scholar
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.Google Scholar
15. Frank, GK. Advances from neuroimaging studies in eating disorders. CNS Spectr. 2015; 20(4): 391–400.Google Scholar
16. Rolls, ET. Taste, olfactory and food texture reward processing in the brain and the control of appetite. Proc Nutr Soc. 2012; 71(4): 488–501.Google Scholar
17. Grabenhorst, F, Rolls, ET. Selective attention to affective value alters how the brain processes taste stimuli. Eur J Neurosci. 2008; 27(3): 723–729.Google Scholar
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): 1549–1559.Google Scholar
19. Hare, TA, Camerer, CF, Rangel, A. Self-control in decision-making involves modulation of the vmPFC valuation system. Science. 2009; 324(5927): 646–648.Google Scholar
20. Lee, MH, Smyser, CD, Shimony, JS. Resting-state fMRI: a review of methods and clinical applications. AJNR Am J Neuroradiol. 2013; 34(10): 1866–1872.Google Scholar
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): 13848–13853.Google Scholar
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): 738–744.Google Scholar
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): 684–692.Google Scholar
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): 945–961.Google Scholar
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.Google Scholar
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.Google Scholar
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): 2132–2141.Google Scholar
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): 15915–15926.Google Scholar
29. Bullmore, E, Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009; 10(3): 186–198.Google Scholar
30. Cortes, C, Vapnik, V. Support-vector networks. Machine Learning. 1995; 20(3): 273–297.Google Scholar
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.Google Scholar
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): 1358–1361.Google Scholar
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.Google Scholar
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.Google Scholar
35. Malik, S, McGlone, F, Dagher, A. State of expectancy modulates the neural response to visual food stimuli in humans. Appetite. 2011; 56(2): 302–309.Google Scholar
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): 24–30.Google Scholar
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): 392–401.Google Scholar
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.Google Scholar
39. Watts, DJ, Strogatz, SH. Collective dynamics of ‘small-world’ networks. Nature. 1998; 393(6684): 440–442.Google Scholar
40. Rubinov, M, Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010; 52(3): 1059–1069.Google Scholar
41. Flake, GW, Lawrence, S, Giles, CL, Coetzee, FM. Self-organization and identification of Web communities. Computer. 2002; 35(3): 66–70.Google Scholar
42. Kleinberg, JM. Authoritative sources in a hyperlinked environment. Journal of the ACM. 1999; 46(5): 604–632.Google Scholar
43. Zalesky, A, Fornito, A, Bullmore, ET. Network-based statistic: identifying differences in brain networks. Neuroimage. 2010; 53(4): 1197–1207.Google Scholar
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): 1404–1412.Google Scholar
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: 141–151.Google Scholar
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): 248–257.Google Scholar
47. Bellman, RE. Dynamic Programming. Mineola, New York. Dover Publications; 2003.Google Scholar
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.Google Scholar
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.Google Scholar
50. Cawley, GC, Talbot, NLC. Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers. Pattern Recognition. 2003; 36(11): 2585–2592.Google Scholar
51. Hastie, T, Tibshirani, R, Friedman, J. The Elements of Statistical Learning. Vol. 2. New York: Springer; 2009.Google Scholar
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): 573–577.Google Scholar
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.Google Scholar
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.Google Scholar
55. Zald, DH. Orbitofrontal cortex contributions to food selection and decision making. Ann Behav Med. 2009; 38(Suppl 1): S18–S24.Google Scholar
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): 1381–1394.Google Scholar
57. Nederkoorn, C, Smulders, FT, Jansen, A. Cephalic phase responses, craving and food intake in normal subjects. Appetite. 2000; 35(1): 45–55.Google Scholar
58. Rolls, ET. The orbitofrontal cortex and reward. Cereb Cortex. 2000; 10(3): 284–294.Google Scholar
59. Staresina, BP, Davachi, L. Differential encoding mechanisms for subsequent associative recognition and free recall. J Neurosci. 2006; 26(36): 9162–9172.Google Scholar
60. Bookheimer, S. Functional MRI of language: new approaches to understanding the cortical organization of semantic processing. Annu Rev Neurosci. 2002; 25: 151–188.Google Scholar
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): 254–256.Google Scholar
62. Zatorre, RJ, Belin, P, Penhune, VB. Structure and function of auditory cortex: music and speech. Trends Cogn Sci. 2002; 6(1): 37–46.Google Scholar
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): 8841–8849.Google Scholar
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): 296–303.Google Scholar
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): 643–653.Google Scholar
66. Simmons, WK, Martin, A, Barsalou, LW. Pictures of appetizing foods activate gustatory cortices for taste and reward. Cereb Cortex. 2005; 15(10): 1602–1608.Google Scholar
67. De Araujo, IE, Rolls, ET. Representation in the human brain of food texture and oral fat. J Neurosci. 2004; 24(12): 3086–3093.Google Scholar
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): 2031–2046.Google Scholar
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.Google Scholar
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): 697–704.Google Scholar
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): 149–158.Google Scholar
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): 989–999.Google Scholar
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): 773–795.Google Scholar
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): 5549–5552.Google Scholar
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.Google Scholar
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.Google Scholar
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): 1297–1305.Google Scholar
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: 159–166.Google Scholar
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): 365–376.Google Scholar
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): 6088–6096.Google Scholar
81. Grill, HJ, Skibicka, KP, Hayes, MR. Imaging obesity: fMRI, food reward, and feeding. Cell Metab. 2007; 6(6): 423–425.Google Scholar
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): 965–971.Google Scholar