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Clinical Studies and Practice

Sex differences in the influence of body mass index on anatomical architecture of brain networks

Abstract

Background/Objectives:

The brain has a central role in regulating ingestive behavior in obesity. Analogous to addiction behaviors, an imbalance in the processing of rewarding and salient stimuli results in maladaptive eating behaviors that override homeostatic needs. We performed network analysis based on graph theory to examine the association between body mass index (BMI) and network measures of integrity, information flow and global communication (centrality) in reward, salience and sensorimotor regions and to identify sex-related differences in these parameters.

Subjects/Methods:

Structural and diffusion tensor imaging were obtained in a sample of 124 individuals (61 males and 63 females). Graph theory was applied to calculate anatomical network properties (centrality) for regions of the reward, salience and sensorimotor networks. General linear models with linear contrasts were performed to test for BMI and sex-related differences in measures of centrality, while controlling for age.

Results:

In both males and females, individuals with high BMI (obese and overweight) had greater anatomical centrality (greater connectivity) of reward (putamen) and salience (anterior insula) network regions. Sex differences were observed both in individuals with normal and elevated BMI. In individuals with high BMI, females compared to males showed greater centrality in reward (amygdala, hippocampus and nucleus accumbens) and salience (anterior mid-cingulate cortex) regions, while males compared to females had greater centrality in reward (putamen) and sensorimotor (posterior insula) regions.

Conclusions:

In individuals with increased BMI, reward, salience and sensorimotor network regions are susceptible to topological restructuring in a sex-related manner. These findings highlight the influence of these regions on integrative processing of food-related stimuli and increased ingestive behavior in obesity, or in the influence of hedonic ingestion on brain topological restructuring. The observed sex differences emphasize the importance of considering sex differences in obesity pathophysiology.

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References

  1. Ogden CL, Carroll MD, Kit BK, Flegal KM . Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 2014; 311: 806–814.

    Article  CAS  Google Scholar 

  2. Lovejoy JC, Sainsbury A, Stock Conference Working G. Sex differences in obesity and the regulation of energy homeostasis. Obes Rev 2009; 10: 154–167.

    Article  CAS  Google Scholar 

  3. Flegal KM, Carroll MD, Ogden CL, Curtin LR . Prevalence and trends in obesity among US adults, 1999-2008. JAMA 2010; 303: 235–241.

    Article  CAS  Google Scholar 

  4. Atalayer D, Pantazatos SP, Gibson CD, McOuatt H, Puma L, Astbury NM et al. Sexually dimorphic functional connectivity in response to high vs. low energy-dense food cues in obese humans: an fMRI study. NeuroImage 2014; 100: 405–413.

    Article  Google Scholar 

  5. Ogden CL, Carroll MD, Kit BK, Flegal KM . Prevalence of obesity in the United States, 2009-2010. NCHS Data Brief 2012; 82: 1–8.

    Google Scholar 

  6. Das UN . Obesity: genes, brain, gut, and environment. Nutrition 2010; 26: 459–473.

    Article  CAS  Google Scholar 

  7. Kenny PJ . Reward mechanisms in obesity: new insights and future directions. Neuron 2011; 69: 664–679.

    Article  CAS  Google Scholar 

  8. Volkow ND, Wang GJ, Baler RD . Reward, dopamine and the control of food intake: implications for obesity. Trends Cogn Sci 2011; 15: 37–46.

    Article  CAS  Google Scholar 

  9. Bartholdy S, Dalton B, O'Daly OG, Campbell IC, Schmidt U . A systematic review of the relationship between eating, weight and inhibitory control using the stop signal task. Neurosci Biobehav Rev 2016; 64: 35–62.

    Article  Google Scholar 

  10. Gupta A, Mayer EA, Sanmiguel CP, Van Horn JD, Woodworth D, Ellingson BM et al. Patterns of brain structural connectivity differentiate normal weight from overweight subjects. NeuroImage 2015; 7: 506–517.

    Article  Google Scholar 

  11. Stice E, Yokum S, Burger KS, Epstein LH, Small DM . Youth at risk for obesity show greater activation of striatal and somatosensory regions to food. J Neurosci 2011; 31: 4360–4366.

    Article  CAS  Google Scholar 

  12. Olivo G, Wiemerslage L, Nilsson EK, Solstrand Dahlberg L, Larsen AL, Olaya Bucaro M et al. Resting-state brain and the FTO obesity risk allele: default mode, sensorimotor, and salience network connectivity underlying different somatosensory integration and reward processing between genotypes. Front Hum Neurosci 2016; 10: 52.

    Article  Google Scholar 

  13. Garcia-Garcia I, Jurado MA, Garolera M, Segura B, Sala-Llonch R, Marques-Iturria I et al. Alterations of the salience network in obesity: a resting-state fMRI study. Hum Brain Mapp 2013; 34: 2786–2797.

    Article  Google Scholar 

  14. Volkow ND, Baler RD . NOW vs LATER brain circuits: implications for obesity and addiction. Trends Neurosci 2015; 38: 345–352.

    Article  CAS  Google Scholar 

  15. Wijngaarden MA, Veer IM, Rombouts SA, van Buchem MA, Willems van Dijk K, Pijl H et al. Obesity is marked by distinct functional connectivity in brain networks involved in food reward and salience. Behav Brain Res 2015; 287: 127–134.

    Article  CAS  Google Scholar 

  16. Carnell S, Gibson C, Benson L, Ochner CN, Geliebter A . Neuroimaging and obesity: current knowledge and future directions. Obes Rev 2012; 13: 43–56.

    Article  CAS  Google Scholar 

  17. 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: e148.

    Article  CAS  Google Scholar 

  18. Kilpatrick LA, Coveleskie K, Connolly L, Labus JS, Ebrat B, Stains J et al. Influence of sucrose ingestion on brainstem and hypothalamic intrinsic oscillations in lean and obese women. Gastroenterology 2014; 146: 1212–1221.

    Article  CAS  Google Scholar 

  19. Connolly L, Coveleskie K, Kilpatrick LA, Labus JS, Ebrat B, Stains J et al. Differences in brain responses between lean and obese women to a sweetened drink. Neurogastroenterol Motil 2013; 25: 579–e460.

    Article  CAS  Google Scholar 

  20. Medic N, Ziauddeen H, Ersche KD, Farooqi IS, Bullmore ET, Nathan PJ et al. Increased body mass index is associated with specific regional alterations in brain structure. Int J Obes 2016; 40: 1177–1182.

    Article  CAS  Google Scholar 

  21. Raji CA, Ho AJ, Parikshak NN, Becker JT, Lopez OL, Kuller LH et al. Brain structure and obesity. Hum Brain Mapp 2010; 31: 353–364.

    PubMed  PubMed Central  Google Scholar 

  22. Shott ME, Cornier MA, Mittal VA, Pryor TL, Orr JM, Brown MS et al. Orbitofrontal cortex volume and brain reward response in obesity. Int J Obes 201439: 214–221.

    Article  Google Scholar 

  23. Stanek KM, Grieve SM, Brickman AM, Korgaonkar MS, Paul RH, Cohen RA et al. Obesity is associated with reduced white matter integrity in otherwise healthy adults. Obesity 2011; 19: 500–504.

    Article  Google Scholar 

  24. Kullmann S, Callaghan MF, Heni M, Weiskopf N, Scheffler K, Haring HU et al. Specific white matter tissue microstructure changes associated with obesity. NeuroImage 2016; 125: 36–44.

    Article  Google Scholar 

  25. Karlsson HK, Tuulari JJ, Hirvonen J, Lepomaki V, Parkkola R, Hiltunen J et al. Obesity is associated with white matter atrophy: a combined diffusion tensor imaging and voxel-based morphometric study. Obesity 2013; 21: 2530–2537.

    Article  Google Scholar 

  26. Volkow ND, Wang GJ, Tomasi D, Baler RD . Obesity and addiction: neurobiological overlaps. Obes Rev 2013; 14: 2–18.

    Article  CAS  Google Scholar 

  27. Hogenkamp PS, Zhou W, Dahlberg LS, Stark J, Larsen AL, Olivo G et al. Higher resting-state activity in reward-related brain circuits in obese versus normal-weight females independent of food intake. Int J Obes 2016; 40: 1687–1692.

    Article  CAS  Google Scholar 

  28. Uher R, Treasure J, Heining M, Brammer MJ, Campbell IC . Cerebral processing of food-related stimuli: effects of fasting and gender. Behav Brain Res 2006; 169: 111–119.

    Article  CAS  Google Scholar 

  29. Geliebter A, Pantazatos SP, McOuatt H, Puma L, Gibson CD, Atalayer D . Sex-based fMRI differences in obese humans in response to high vs. low energy food cues. Behav Brain Res 2013; 243: 91–96.

    Article  Google Scholar 

  30. Zhang B, Tian D, Yu C, Zhang J, Tian X, von Deneen KM et al. Altered baseline brain activities before food intake in obese men: a resting state fMRI study. Neurosci Lett 2015; 584: 156–161.

    Article  CAS  Google Scholar 

  31. Haber SN, Knutson B . The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology 2010; 35: 4–26.

    Article  Google Scholar 

  32. Vicente AF, Bermudez MA, Romero Mdel C, Perez R, Gonzalez F . Putamen neurons process both sensory and motor information during a complex task. Brain Res 2012; 1466: 70–81.

    Article  CAS  Google Scholar 

  33. Balleine BW, Delgado MR, Hikosaka O . The role of the dorsal striatum in reward and decision-making. J Neurosci 2007; 27: 8161–8165.

    Article  CAS  Google Scholar 

  34. Rubinov M, Sporns O . Complex network measures of brain connectivity: uses and interpretations. NeuroImage 2010; 52: 1059–1069.

    Article  Google Scholar 

  35. Bullmore E, Sporns O . Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 2009; 10: 186–198.

    Article  CAS  Google Scholar 

  36. Sporns O . Structure and function of complex brain networks. Dialogues Clin Neurosci 2013; 15: 247–262.

    PubMed  PubMed Central  Google Scholar 

  37. Kroenke K, Spitzer RL, Williams JB . The PHQ-15: validity of a new measure for evaluating the severity of somatic symptoms. Psychosomatic Med 2002; 64: 258–266.

    Article  Google Scholar 

  38. Cohen S, Kamarck T, Mermelstein R . A global measure of perceived stress. J Health Soc Behav 1983; 24: 385–396.

    Article  CAS  Google Scholar 

  39. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 2002; 33: 341–355.

    Article  CAS  Google Scholar 

  40. Dale AM, Fischl B, Sereno MI . Cortical surface-based analysis - I. Segmentation and surface reconstruction. NeuroImage 1999; 9: 179–194.

    Article  CAS  Google Scholar 

  41. Destrieux C, Fischl B, Dale A, Halgren E . Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage 2010; 53: 1–15.

    Article  Google Scholar 

  42. van den Heuvel MP, Sporns O . Rich-club organization of the human connectome. J Neurosci 2011; 31: 15775–15786.

    Article  CAS  Google Scholar 

  43. van den Heuvel MP, Sporns O, Collin G, Scheewe T, Mandl RC, Cahn W et al. Abnormal rich club organization and functional brain dynamics in schizophrenia. JAMA Psychiatry 2013; 70: 783–792.

    Article  Google Scholar 

  44. Bonilha L, Gleichgerrcht E, Fridriksson J, Rorden C, Breedlove JL, Nesland T et al. Reproducibility of the structural brain connectome derived from diffusion tensor imaging. Plos One 2015; 10: e0135247.

    Article  Google Scholar 

  45. Fischi-Gomez E, Vasung L, Meskaldji DE, Lazeyras F, Borradori-Tolsa C, Hagmann P et al. Structural brain connectivity in school-age preterm infants provides evidence for impaired networks relevant for higher order cognitive skills and social cognition. Cerebral cortex 2015; 25: 2793–2805.

    Article  Google Scholar 

  46. Kubicki M, Park H, Westin CF, Nestor PG, Mulkern RV, Maier SE et al. DTI and MTR abnormalities in schizophrenia: analysis of white matter integrity. NeuroImage 2005; 26: 1109–1118.

    Article  CAS  Google Scholar 

  47. van den Heuvel MP, Stam CJ, Kahn RS, Hulshoff Pol HE . Efficiency of functional brain networks and intellectual performance. J Neurosci 2009; 29: 7619–7624.

    Article  CAS  Google Scholar 

  48. Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci 2007; 27: 2349–2356.

    Article  CAS  Google Scholar 

  49. Stice E, Burger KS, Yokum S . Relative ability of fat and sugar tastes to activate reward, gustatory, and somatosensory regions. Am J Clin Nutr 2013; 98: 1377–1384.

    Article  CAS  Google Scholar 

  50. Opsahl T, Agneessens F, Skortez J . Node centrality in weighted networks: generalizing degree and shortest paths. Social Netw 2010; 32: 245–251.

    Article  Google Scholar 

  51. Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE . Permutation inference for the general linear model. NeuroImage 2014; 92: 381–397.

    Article  Google Scholar 

  52. Benjamini Y, Krieger AM, Yekutieli D . Adaptive linear step-up procedures that control the false discovery rate. Biometrika 2006; 93: 491–507.

    Article  Google Scholar 

  53. Fritz CO, Morris PE, Richler JJ . Effect size estimates: current use, calculations, and interpretation. J Exp Psychol Gen 2012; 141: 2–18.

    Article  Google Scholar 

  54. Dang LC, Samanez-Larkin GR, Castrellon JJ, Perkins SF, Cowan RL, Zald DH . Associations between dopamine D2 receptor availability and BMI depend on age. NeuroImage 2016; 138: 176–83.

    Article  CAS  Google Scholar 

  55. 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: 1537–1543.

    Article  Google Scholar 

  56. Dietrich A, Hollmann M, Mathar D, Villringer A, Horstmann A . Brain regulation of food craving: relationships with weight status and eating behavior. Int J Obes 2016; 40: 982–9.

    Article  CAS  Google Scholar 

  57. Park BY, Seo J, Yi J, Park H . Structural and functional brain connectivity of people with obesity and prediction of body mass index using connectivity. Plos One 2015; 10: e0141376.

    Article  Google Scholar 

  58. Marques-Iturria I, Scholtens LH, Garolera M, Pueyo R, Garcia-Garcia I, Gonzalez-Tartiere P et al. Affected connectivity organization of the reward system structure in obesity. NeuroImage 2015; 111: 100–106.

    Article  CAS  Google Scholar 

  59. Cohen MX, Schoene-Bake JC, Elger CE, Weber B . Connectivity-based segregation of the human striatum predicts personality characteristics. Nat Neurosci 2009; 12: 32–34.

    Article  CAS  Google Scholar 

  60. Xu J, Li Y, Lin H, Sinha R, Potenza MN . Body mass index correlates negatively with white matter integrity in the fornix and corpus callosum: a diffusion tensor imaging study. Hum Brain Mapp 2013; 34: 1044–1052.

    Article  Google Scholar 

  61. Shott ME, Cornier MA, Mittal VA, Pryor TL, Orr JM, Brown MS et al. Orbitofrontal cortex volume and brain reward response in obesity. Int J Obes 2015; 39: 214–221.

    Article  CAS  Google Scholar 

  62. Riederer JW, Shott ME, Deguzman M, Pryor TL, Frank GK . Understanding neuronal architecture in obesity through analysis of white matter connection strength. Front Hum Neurosci 2016; 10: 271.

    Article  Google Scholar 

  63. Rapuano KM, Huckins JF, Sargent JD, Heatherton TF, Kelley WM . Individual differences in reward and somatosensory-motor brain regions correlate with adiposity in adolescents. Cerebral Cortex 2016; 26: 2602–2611.

    Article  Google Scholar 

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Acknowledgements

This research was supported by grants from the National Institutes of Health including K23 DK106528 (AG), R01 DK048351 (EAM), P50 DK064539 (EAM), P30 DK041301, R01 AT007137 (KT), R03 DK084169 (JSL), and pilot funds were provided for brain scanning by the Ahmanson-Lovelace Brain Mapping Center.

Author contributions

Study concept and design, analysis and interpretation of the data, drafting and revision of manuscript were done by AG. Drafting and critical review of manuscript, approval of final version of the manuscript, interpretation of the data and study funding were done by EM. Generation of the data and data analysis were done by KH. Analysis of the data was done by RB, CF, MA, CT, CA-M and JDVH. BN and KT reviewed the manuscript and did the study funding. CPS drafted and critically reviewed the manuscript. JSL helped with the study concept and design, analyzed the data, drafted and revised the manuscript, approved the final version of the manuscript.

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Correspondence to A Gupta.

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Supplementary Information accompanies this paper on International Journal of Obesity website

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Gupta, A., Mayer, E., Hamadani, K. et al. Sex differences in the influence of body mass index on anatomical architecture of brain networks. Int J Obes 41, 1185–1195 (2017). https://doi.org/10.1038/ijo.2017.86

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