Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Pediatrics

Structural connectivity and weight loss in children with obesity: a study of the “connectobese”

Abstract

Background

Previous studies suggest that obesity (OB) is associated with disrupted brain network organization; however, it remains unclear whether these differences already exist during childhood. Moreover, it should be investigated whether deviant network organization may be susceptible to treatment.

Methods

Here, we compared the structural connectomes of children with OB with age-matched healthy weight (HW) controls (aged 7–11 years). In addition, we examined the effect of a multidisciplinary treatment program, consisting of diet restriction, cognitive behavioral therapy, and physical activity for children with OB on brain network organization. After stringent quality assessment criteria, 40 (18 OB, 22 HW) data sets of the total sample of 51 participants (25 OB, 26 HW) were included in further analyses. For all participants, anthropometric measurements were administered twice, with a 5-month interval between pre- and post tests. Pre- and post T1- and diffusion-weighted imaging scans were also acquired and analyzed using a graph-theoretical approach and network-based statistics.

Results

Global network analyses revealed a significantly increased normalized clustering coefficient and small-worldness in children with OB compared with HW controls. In addition, regional analyses revealed increased betweenness centrality, reduced clustering coefficient, and increased structural network strength in children with OB, mainly in the motor cortex and reward network. Importantly, children with OB lost a considerable amount of their body mass after the treatment; however, no changes were observed in the organization of their brain networks.

Conclusion

This is the first study showing disrupted structural connectomes of children with OB, especially in the motor and reward network. These results provide new insights into the pathophysiology underlying childhood obesity. The treatment did result in a significant weight loss, which was however not associated with alterations in the brain networks. These findings call for larger samples to examine the impact of short-term and long-term weight loss (treatment) on children’s brain network organization.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Sahoo K, Sahoo B, Choudhury AK, Sufi NY, Kumar R, Bhadoria AS. Childhood obesity: causes and consequences. J Fam Med Prim Care. 2015;4:187–92.

    Article  Google Scholar 

  2. Keating CL, Moodie ML, Swinburn BA. The health-related quality of life of overweight and obese adolescents – a study measuring body mass index and adolescent-reported perceptions. Int J Pediatr Obes. 2011;6:434–41.

    Article  PubMed  Google Scholar 

  3. Wijnhoven TM, van Raaij JM, Yngve A, Sjöberg A, Kunešová M, Duleva V, et al. WHO European Childhood Obesity Surveillance Initiative: health-risk behaviours on nutrition and physical activity in 6–9-year-old school children. Public Health Nutr. 2015;18:3108–24.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Robinson LE, Stodden DF, Barnett LM, Lopes VP, Logan SW, Rodrigues LP, et al. Motor competence and its effect on positive developmental trajectories of health. Sport Med. 2015;45:1273–84.

    Article  Google Scholar 

  5. Joseph RJ, Alonso-Alonso M, Bond DS, Pascual-Leone A, Blackburn GL. The neurocognitive connection between physical activity and eating behaviour. Obes Rev. 2011;12:800–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Liang J, Matheson BE, Kaye WH, Boutelle KN. Neurocognitive correlates of obesity and obesity-related behaviors in children and adolescents. Int J Obes. 2014;38:494–506.

    Article  CAS  Google Scholar 

  7. Hansen CJ, Stevens LC, Coast JR. Exercise duration and mood state: how much is enough to feel better? Heal Psychol. 2001;20:267–75.

    Article  CAS  Google Scholar 

  8. Ou X, Andres A, Pivik RT, Cleves MA, Badger TM. Brain gray and white matter differences in healthy normal weight and obese children. J Magn Reson Imaging. 2015;42:1205–13.

    Article  PubMed  Google Scholar 

  9. Moreno-López L, Soriano-Mas C, Delgado-Rico E, Rio-Valle JS, Verdejo-García A. Brain structural correlates of reward sensitivity and impulsivity in adolescents with normal and excess weight. PLoS ONE. 2012;7:e49185.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Augustijn MJCM, Deconinck FJA, D’Hondt E, Van Acker L, De Guchtenaere A, Lenoir M, et al. Reduced motor competence in children with obesity is associated with structural differences in the cerebellar peduncles. Brain Imaging Behav. 2018;12:1000–10.

  11. MJCM Augustijn, D’Hondt E, Leemans A, Van Acker L, De Guchtenaere A, Lenoir M, et al. Weight loss, behavioural change and structural neuroplasticity in children with obesity through a multidisciplinary treatment program. Hum Brain Mapp. 2018;12:1000–10.

    Google Scholar 

  12. Bressler SL, Menon V. Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn Sci. 2010;14:277–90.

    Article  PubMed  Google Scholar 

  13. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52:1059–69.

    Article  PubMed  Google Scholar 

  14. Bassett DS, Bullmore E. Small-world brain networks. Neuroscientist. 2006;12:512–23.

    Article  PubMed  Google Scholar 

  15. Bruce AS, Martin LE, Savage CR. Neural correlates of pediatric obesity. Prev Med. 2011;52:S29–35.

    Article  PubMed  Google Scholar 

  16. Sporns O, Tononi G, Kötter R. The human connectome: a structural description of the human brain. PLoS Comput Biol. 2005;1:0245–51.

    Article  CAS  Google Scholar 

  17. Griffa A, Baumann PS, Thiran JP, Hagmann P. Structural connectomics in brain diseases. Neuroimage. 2013;80:515–26.

    Article  PubMed  Google Scholar 

  18. Chao S-H, Liao Y-T, Chen VC-H, Li C-J, McIntyre RS, Lee Y, et al. Correlation between brain circuit segregation and obesity. Behav Brain Res. 2018;337:218–27.

    Article  PubMed  Google Scholar 

  19. Baek K, Morris LS, Kundu P, Voon V. Disrupted resting-state brain network properties in obesity: decreased global and putaminal cortico-striatal network efficiency. Psychol Med. 2017;47:585–96.

    Article  CAS  PubMed  Google Scholar 

  20. Marqués-Iturria I, Scholtens LH, Garolera M, Pueyo R, García-García I, González-Tartiere P, et al. Affected connectivity organization of the reward system structure in obesity. Neuroimage. 2015;111:100–6.

    Article  PubMed  Google Scholar 

  21. Yuan W, Treble-Barna A, Sohlberg MM, Harn B, Wade SL. Changes in structural connectivity following a cognitive intervention in children with traumatic brain injury. Neurorehabil Neural Repair. 2017;31:190–201.

    Article  PubMed  Google Scholar 

  22. Yuan W, Wade SL, Quatman-Yates C, Hugentobler JA, Gubanich PJ, Kurowski BG. Structural connectivity related to persistent symptoms after mild tbi in adolescents and response to aerobic training: preliminary investigation. J Head Trauma Rehabil. 2017;32:378–84.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Amidi A, Hosseini SMH, Leemans A, Kesler SR, Agerbæk M, Wu LM, et al. Changes in brain structural networks and cognitive functions in testicular cancer patients receiving cisplatin-based chemotherapy. J Natl Cancer Inst. 2017;109:1–7.

    Article  Google Scholar 

  24. Langer N, von Bastian CC, Wirz H, Oberauer K, Jäncke L. The effects of working memory training on functional brain network efficiency. Cortex. 2013;49:2424–38.

    Article  PubMed  Google Scholar 

  25. Caeyenberghs K, Metzler-Baddeley C, Foley S, Jones DK. Dynamics of the human structural connectome underlying working memory training. J Neurosci. 2016;36:4056–66.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks. Neuroimage. 2010;53:1197–207.

    Article  PubMed  Google Scholar 

  27. Cole TJ, Lobstein T. Extended international (IOTF) body mass index cut-offs for thinness, overweight and obesity. Pediatr Obes. 2012;7:284–94.

    Article  CAS  PubMed  Google Scholar 

  28. D’Hondt E, Gentier I, Deforche B, Tanghe A, De Bourdeaudhuij I, Lenoir M. Weight loss and improved gross motor coordination in children as a result of multidisciplinary residential obesity treatment. Obesity. 2011;19:1999–2005.

    Article  PubMed  Google Scholar 

  29. Gentier I, D’Hondt E, Augustijn M, Tanghe A, De Bourdeaudhuij I, Deforche B, et al. Multidisciplinary residential treatment can improve perceptual-motor function in obese children. Acta Paediatr. 2015;104:e263–70.

    Article  PubMed  Google Scholar 

  30. Goscinski WJ, McIntosh P, Felzmann U, Maksimenko A, Hall CJ, Gureyev T, et al. The Multi-modal Australian ScienceS Imaging and Visualization Environment (MASSIVE) high performance computing infrastructure: applications in neuroscience and neuroinformatics research. Front Neuroinform. 2014;8:1–13.

    Article  Google Scholar 

  31. Roberts JA, Perry A, Lord AR, Roberts G, Mitchell PB, Smith RE, et al. The contribution of geometry to the human connectome. Neuroimage. 2016;124:379–93.

    Article  PubMed  Google Scholar 

  32. Zalesky A, Fornito A, Cocchi L, Gollo LL, van den Heuvel MP, Breakspear M. Connectome sensitivity or specificity: which is more important? Neuroimage. 2016;142:407–20.

    Article  PubMed  Google Scholar 

  33. Kaiser M, Hilgetag CC. Nonoptimal component placement, but short processing paths, due to long-distance projections in neural systems. PLoS Comput Biol. 2006;2:0805–15.

    Article  CAS  Google Scholar 

  34. Hosseini SMH, Hoeft F, Kesler SR, Lambiotte R. GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks. PLoS ONE. 2012;7:e40709.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. De Reus MA, Van Den Heuvel MP. Estimating false positives and negatives in brain networks. Neuroimage. 2013;70:402–9.

    Article  PubMed  Google Scholar 

  36. Marshall WA, Tanner JM. Variations in the pattern of pubertal changes in boys. Arch Dis Child. 1970;45:13–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Caeyenberghs K, Clemente A, Imms P, Egan G, Hocking DR, Leemans A, et al. Evidence for training-dependent structural neuroplasticity in brain-injured patients: a critical review. Neurorehabil Neural Repair. 2018;32:99–114.

    Article  PubMed  Google Scholar 

  38. Hagmann P, Sporns O, Madan N, Cammoun L, Pienaar R, Wedeen VJ, et al. White matter maturation reshapes structural connectivity in the late developing human brain. Proc Natl Acad Sci USA. 2010;107:19067–72.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Collin G, Van Den, Heuvel MP. The ontogeny of the human connectome: Development and dynamic changes of brain connectivity across the life span. Neuroscientist. 2013;19:616–28.

    Article  CAS  PubMed  Google Scholar 

  40. Fair DA, Cohen AL, Power JD, Dosenbach NUF, Church JA, Miezin FM, et al. Functional brain networks develop from a “Local to Distributed” organization. PLoS Comput Biol. 2009;5:e1000381.

  41. Wang L, Zhu C, He Y, Zang Y, Cao Q, Zhang H, et al. Altered small-world brain functional networks in children with attention-deficit/hyperactivity disorder. Hum Brain Mapp. 2009;30:638–49.

    Article  CAS  PubMed  Google Scholar 

  42. Di Martino A, Fair DA, Kelly C, Satterthwaite TD, Castellanos FX, Thomason ME, et al. Unraveling the miswired connectome: a developmental perspective. Neuron. 2014;83:1335–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Caeyenberghs K, Taymans T, Wilson PH, Vanderstraeten G, Hosseini H, van Waelvelde H. Neural signature of developmental coordination disorder in the structural connectome independent of comorbid autism. Dev Sci. 2016;19:599–612.

    Article  PubMed  Google Scholar 

  44. Rizzolatti G, Luppino G. The cortical motor system. Neuron. 2001;31:889–901.

    Article  CAS  PubMed  Google Scholar 

  45. D’Hondt E, Deforche B, De Bourdeaudhuij I, Lenoir M. Childhood obesity affects fine motor skill performance under different postural constraints. Neurosci Lett. 2008;440:72–5.

    Article  CAS  PubMed  Google Scholar 

  46. D’Hondt E, Deforche B, De Bourdeaudhuij I, Lenoir M. Relationship between motor skill and body mass index in 5- to 10-year-old children. Adapt Phys Activ Q. 2009;26:21–37.

    Article  Google Scholar 

  47. Gentier I, D’Hondt E, Shultz S, Deforche B, Augustijn M, Hoorne S, et al. Fine and gross motor skills differ between healthy-weight and obese children. Res Dev Disabil. 2013;34:4043–51.

    Article  PubMed  Google Scholar 

  48. Sporns O, Honey CJ, Kötter R. Identification and classification of hubs in brain networks. PLoS ONE. 2007;2:e1049.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Rudebeck PH, Murray EA. The orbitofrontal oracle: cortical mechanisms for the prediction and evaluation of specific behavioral outcomes. Neuron. 2014;84:1143–56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  51. van den Berg L, Pieterse K, Malik JA, Luman M, Willems van Dijk K, Oosterlaan J, et al. Association between impulsivity, reward responsiveness and body mass index in children. Int J Obes. 2011;35:1301–7.

    Article  Google Scholar 

  52. Verdejo-García A, Verdejo-Román J, Rio-Valle JS, Lacomba JA, Lagos FM, Soriano-Mas C. Dysfunctional involvement of emotion and reward brain regions on social decision making in excess weight adolescents. Hum Brain Mapp. 2015;36:226–37.

    Article  PubMed  Google Scholar 

  53. Kravitz AV, O’Neal TJ, Friend DM. Do dopaminergic impairments underlie physical inactivity in people with obesity? Front Hum Neurosci. 2016;10:1–8. (514)

    Article  CAS  Google Scholar 

  54. Diamond A. Executive functions. Annu Rev Psychol. 2013;64:135–68.

    Article  PubMed  Google Scholar 

  55. Casey BJ, Giedd JN, Thomas KM. Structural and functional brain development and its relation to cognitive development. Biol Psychol. 2000;54:241–57.

    Article  CAS  PubMed  Google Scholar 

  56. Gogtay N, Giedd JN, Lusk L, Hayashi KM, Greenstein D, Vaituzis a C, et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc Natl Acad Sci USA. 2004;101:8174–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Willoughby T, Good M, Adachi PJC, Hamza C, Tavernier R. Examining the link between adolescent brain development and risk taking from a social-developmental perspective. Brain Cogn. 2014;89:70–8.

    Article  PubMed  Google Scholar 

  58. Deforche B, De Bourdeaudhuij I, Debode P, Vinaimont F, Hills AP, Verstraete S, et al. Changes in fat mass, fat-free mass and aerobic fitness in severely obese children and adolescents following a residential treatment programme. Eur J Pediatr. 2003;162:616–22.

    Article  PubMed  Google Scholar 

  59. Braet C, Tanghe A, Decaluwé V, Moens E, Rosseel Y. Inpatient treatment for children with obesity: weight loss, psychological well-being, and eating behavior. J Pediatr Psychol. 2004;29:519–29.

    Article  PubMed  Google Scholar 

  60. Weise CM, Thiyyagura P, Reiman EM, Chen K, Krakoff J. Fat-free body mass but not fat mass is associated with reduced gray matter volume of cortical brain regions implicated in autonomic and homeostatic regulation. Neuroimage. 2013;64:712–21.

    Article  PubMed  Google Scholar 

  61. Thomas C, Baker CI. Teaching an adult brain new tricks: a critical review of evidence for training-dependent structural plasticity in humans. Neuroimage. 2013;73:225–36.

    Article  PubMed  Google Scholar 

  62. Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. FSL. Neuroimage. 2012;62:782–90.

    Article  PubMed  Google Scholar 

  63. Tournier J-DD, Calamante F, Connelly A. MRtrix: diffusion tractography in crossing fiber regions. Int J Imaging Syst Technol. 2012;22:53–66.

    Article  Google Scholar 

  64. Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31:968–80.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The study was funded by the Ph.D. fellowship of the Research Foundation Flanders (FWO) awarded to MJCMA [3F000714]. The authors are very grateful to all participants and their parents, the staff from the rehabilitation center “Zeepreventorium” (De Haan, Belgium), and the board of the participating schools.

Funding

This study was funded by the Ph.D. fellowship of the Research Foundation Flanders (FWO) awarded to MJCMA [3F000714].

Author information

Authors and Affiliations

Authors

Contributions

Conception and design of the experiment: MJCMA, FJAD, ED’H, ML, and KC. Collection and processing of the data: MJCMA, LVA, MADB, and AZ. Interpretation of the results: MJCMA, MADB, FJAD, ED’H, ML, KC, and AZ. Drafting of the paper and critical revision: MJCMA, MADB, FJAD, ED’H, ML, KC, AZ, ADG, and LVA. All authors had final approval of the submitted and published version.

Corresponding author

Correspondence to Mireille J. C. M. Augustijn.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Augustijn, M.J.C.M., Di Biase, M.A., Zalesky, A. et al. Structural connectivity and weight loss in children with obesity: a study of the “connectobese”. Int J Obes 43, 2309–2321 (2019). https://doi.org/10.1038/s41366-019-0380-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41366-019-0380-6

This article is cited by

Search

Quick links