Age-associated reorganization of metabolic brain connectivity in Chinese children

  • Qi Huang
  • Jian Zhang
  • Tianhao Zhang
  • Hui WangEmail author
  • Jianhua YanEmail author
Original Article
Part of the following topical collections:
  1. Neurology



The human brain develops rapidly from infant to adolescent. Establishment of the brain developmental trajectory is important to understand cognition, behavior, and emotions, as well to evaluate the risk of neuropsychiatric disorders. 18F-FDG PET has been widely used to study brain glucose metabolism, but functional brain segregation and integration based on 18F-FDG PET remains largely unknown.


Two hundred one Chinese child patients with extracranial malignancy were retrospectively enrolled as surrogates to healthy children. All images were spatially normalized into MNI space using pediatric brain template, and the 18F-FDG uptakes were calculated for 90 regions using AAL atlas. The group-level metabolic brain network was constructed by measuring Pearson correlation coefficients between each pair of brain regions in an inter-subject manner for infant (1 to 4 years), childhood (5 to 8 years), early adolescent (9 to 12 years), and adolescent (13 to 18 years) group, respectively. Global efficiency of each group was calculated, and the modular architectures were detected by a greedy algorithm.


Both metabolic brain network connectivity and global efficiency increased with aging. Brain network was grouped into 4, 6, 4, and 4 modules from infant to adolescent, respectively. The modular architecture dramatically reorganized from childhood to early adolescent. The hubs spatiotemporally rewired. The ratio of the connector hub to the provincial hub increased from infant to early adolescent, but decreased during the adolescent period.


The topological properties and modular reorganization of human brain network dramatically changed with age, especially from childhood to early adolescence. These findings would help understand the Chinese developmental trajectory of human brain functional integration and segregation.


Brain development Metabolic brain network Modularity 18F-FDG PET 


Funding information

This study was sponsored by the Shanghai Sailing Program (19YF1405300) and startup fund of Huashan Hospital, Fudan University (837) to QH, and National Natural Science Foundation of China (81671775) to JY.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the principles of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Informed consent

Consent forms were obtained from the parents or guardians of subjects (1–16 years) and participants (above 16 years) in the study.


  1. 1.
    London K, Howman-Giles R. Normal cerebral FDG uptake during childhood. Eur J Nucl Med Mol Imaging. 2014;41:723–35. Scholar
  2. 2.
    Shan ZY, Leiker AJ, Onar-Thomas A, Li Y, Feng T, Reddick WE, et al. Cerebral glucose metabolism on positron emission tomography of children. Hum Brain Mapp. 2014;35:2297–309. Scholar
  3. 3.
    Hua C, Merchant TE, Li X, Li Y, Shulkin BL. Establishing age-associated normative ranges of the cerebral 18F-FDG uptake ratio in children. J Nucl Med. 2015;56:575–9.CrossRefGoogle Scholar
  4. 4.
    Gilmore JH, Knickmeyer RC, Gao W. Imaging structural and functional brain development in early childhood. Nat Rev Neurosci. 2018;19:123–37.CrossRefGoogle Scholar
  5. 5.
    Meunier D, Lambiotte R, Bullmore ET. Modular and hierarchically modular organization of brain networks. Front Neurosci. 2010;4:200.CrossRefGoogle Scholar
  6. 6.
    Girvan M, Newman MEJ. Community structure in social and biological networks. 2001.Google Scholar
  7. 7.
    He Y, Wang J, Wang L, Chen ZJ, Yan C, Yang H, et al. Uncovering intrinsic modular organization of spontaneous brain activity in humans. PLoS One. 2009;4:e5226. Scholar
  8. 8.
    Salvador R, Suckling J, Coleman MR, Pickard JD, Menon D, Bullmore E. Neurophysiological architecture of functional magnetic resonance images of human brain. Cereb Cortex. 2005;15:1332–42.CrossRefGoogle Scholar
  9. 9.
    Zhang T, Huang Q, Jiao C, Liu H, Nie B, Liang S, et al. Modular architecture of metabolic brain network and its effects on the spread of perturbation impact. Neuroimage. 2018;186:146–54. Scholar
  10. 10.
    Guimerà R, Amaral LAN. Functional cartography of complex metabolic networks. Nature. 2005;433:895.CrossRefGoogle Scholar
  11. 11.
    Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL, et al. Unbiased average age-appropriate atlases for pediatric studies. Neuroimage. 2011;54:313–27. Scholar
  12. 12.
    Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15:273–89.CrossRefGoogle Scholar
  13. 13.
    Van Wijk BC, Stam CJ, Daffertshofer A. Comparing brain networks of different size and connectivity density using graph theory. PloS one. 2010;5:e13701.CrossRefGoogle Scholar
  14. 14.
    Rubinov M, Sporns O. complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52:1059–69. Scholar
  15. 15.
    Sporns O, Betzel RF. Modular brain networks. In: Fiske ST, editor. Annu Rev Psychol, Vol 67; 2016. p. 613–640.Google Scholar
  16. 16.
    Newman MEJ. Analysis of weighted networks. Phys Rev E. 2004;70.
  17. 17.
    Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech-Theory Exp. 2008.
  18. 18.
    Meunier D, Achard S, Morcom A, Bullmore E. Age-related changes in modular organization of human brain functional networks. Neuroimage. 2009;44:715–23.CrossRefGoogle Scholar
  19. 19.
    Wen X, Zhang H, Li G, Liu M, Yin W, Lin W, et al. First-year development of modules and hubs in infant brain functional networks. NeuroImage. 2019;185:222–35. Scholar
  20. 20.
    Chen ZJ, He Y, Rosa P, Germann J, Evans AC. Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. Cereb Cortex. 2008;18:2374–81. Scholar
  21. 21.
    Raymond S, John S, Coleman MR, Pickard JD, David M, Ed B. Neurophysiological architecture of functional magnetic resonance images of human brain. Cereb Cortex. 2005;15:387–413.Google Scholar
  22. 22.
    Mp VDH, Stam CJKahn RS. Efficiency of functional brain networks and intellectual performance. J Neurosci. 2009;29:7619–24.CrossRefGoogle Scholar
  23. 23.
    Andersen SL. Trajectories of brain development: point of vulnerability or window of opportunity? Neurosci Biobehav Rev. 2003;27:3–18.CrossRefGoogle Scholar
  24. 24.
    Bullmore E, Sporns O. The economy of brain network organization. Nat Rev Neurosci. 2012;13:336.CrossRefGoogle Scholar
  25. 25.
    Schneider P, Scherg M, Dosch HG, Specht HJ, Gutschalk A, Rupp A. Morphology of Heschl’s gyrus reflects enhanced activation in the auditory cortex of musicians. Nat Neurosci. 2002;5:688.CrossRefGoogle Scholar
  26. 26.
    Downing PE, Jiang Y, ., Shuman M, ., Kanwisher N,. A cortical area selective for visual processing of the human body. Science. 2001;293:2470–2473.CrossRefGoogle Scholar
  27. 27.
    Dierks T, Linden DE, Jandl M, Formisano E, Goebel R, Lanfermann H, et al. Activation of Heschl’s gyrus during auditory hallucinations. Neuron. 1999;22:615–21.CrossRefGoogle Scholar
  28. 28.
    Collignon O, Vandewalle G, Voss P, Albouy G, Charbonneau G, Lassonde M, et al. Functional specialization for auditory–spatial processing in the occipital cortex of congenitally blind humans. Proc Natl Acad Sci. 2011;108:4435–40. Scholar
  29. 29.
    Malikovic A, Vucetic B, Milisavljevic M, Tosevski J, Sazdanovic P, Milojevic B, et al. Occipital sulci of the human brain: variability and morphometry. Anat Sci Int. 2012;87:61–70. Scholar
  30. 30.
    Braddick OJ, O’Brien JM, Wattam-Bell J, Atkinson J, Hartley T, Turner R. Brain areas sensitive to coherent visual motion. Perception. 2001;30:61–72.CrossRefGoogle Scholar
  31. 31.
    Kerns JG, Cohen JD, MacDonald AW, Cho RY, Stenger VA, Carter CS. Anterior cingulate conflict monitoring and adjustments in control. Science. 2004;303:1023–6. Scholar
  32. 32.
    Rainville P, Duncan GH, Price DD, Carrier B, Bushnell MC. Pain affect encoded in human anterior cingulate but not somatosensory cortex. Science. 1997;277:968–71.CrossRefGoogle Scholar
  33. 33.
    Graziano MS, Taylor CS, Moore T. Complex movements evoked by microstimulation of precentral cortex. Neuron. 2002;34:841–51.CrossRefGoogle Scholar
  34. 34.
    Kaas JH, Sur M, Nelson RJ, Merzenich MM. The postcentral somatosensory cortex. Berlin: Cortical sensory organization: Springer; 1981. p. 29–45.Google Scholar
  35. 35.
    Brunner R, Henze R, Parzer P, Kramer J, Feigl N, Lutz K, et al. Reduced prefrontal and orbitofrontal gray matter in female adolescents with borderline personality disorder: is it disorder specific? Neuroimage. 2010;49:114–20.CrossRefGoogle Scholar
  36. 36.
    Chanen AM, Velakoulis D, Carison K, Gaunson K, Wood SJ, Yuen HP, et al. Orbitofrontal, amygdala and hippocampal volumes in teenagers with first-presentation borderline personality disorder. Psychiatry Res Neuroimaging. 2008;163:116–25.CrossRefGoogle Scholar
  37. 37.
    Cohen MX, Schoene-Bake J-C, Elger CE, Weber B. Connectivity-based segregation of the human striatum predicts personality characteristics. Nat Neurosci. 2008;12:32. Scholar
  38. 38.
    Bachevalier J, Loveland KA. The orbitofrontal–amygdala circuit and self-regulation of social–emotional behavior in autism. Neurosci Biobehav Rev. 2006;30:97–117.CrossRefGoogle Scholar
  39. 39.
    Galletti C, Fattori P, Battaglini P, Shipp S, Zeki S. Functional demarcation of a border between areas V6 and V6A in the superior parietal gyrus of the macaque monkey. Eur J Neurosci. 1996;8:30–52.CrossRefGoogle Scholar
  40. 40.
    Sobel N, Prabhakaran V, Desmond J, Glover G, Goode R, Sullivan E, et al. Sniffing and smelling: separate subsystems in the human olfactory cortex. Nature. 1998;392:282.CrossRefGoogle Scholar
  41. 41.
    Belliveau J, Kennedy D, McKinstry R, Buchbinder B, Weisskoff R, Cohen M, et al. Functional mapping of the human visual cortex by magnetic resonance imaging. Science. 1991;254:716–9.CrossRefGoogle Scholar
  42. 42.
    Bush G, Luu P, Posner MI. Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn Sci. 2000;4:215–22.CrossRefGoogle Scholar
  43. 43.
    Maddock RJ, Garrett AS, Buonocore MH. Posterior cingulate cortex activation by emotional words: fMRI evidence from a valence decision task. Hum Brain Mapp. 2003;18:30–41.CrossRefGoogle Scholar
  44. 44.
    Buckner RL, Kelley WM, Petersen SE. Frontal cortex contributes to human memory formation. Nat Neurosci. 1999;2:311.CrossRefGoogle Scholar
  45. 45.
    Rushworth MF, Noonan MP, Boorman ED, Walton ME, Behrens TE. Frontal cortex and reward-guided learning and decision-making. Neuron. 2011;70:1054–69.CrossRefGoogle Scholar
  46. 46.
    Bechara A, Damasio H, Damasio AR. Emotion, decision making and the orbitofrontal cortex. Cereb Cortex. 2000;10:295–307.CrossRefGoogle Scholar
  47. 47.
    Gleichgerrcht E, Ibáñez A, Roca M, Torralva T, Manes F. Decision-making cognition in neurodegenerative diseases. Nat Rev Neurol. 2010;6:611.CrossRefGoogle Scholar
  48. 48.
    Paulus MP. Decision-making dysfunctions in psychiatry—altered homeostatic processing? Science. 2007;318:602–6.CrossRefGoogle Scholar
  49. 49.
    Rajmohan V, Mohandas E. The limbic system. Indian J Psychiatry. 2007;49:132.CrossRefGoogle Scholar
  50. 50.
    Gogtay N, Giedd JN, Lusk L, Hayashi KM, Greenstein D, Vaituzis AC, et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc Natl Acad Sci. 2004;101:8174–9.CrossRefGoogle Scholar
  51. 51.
    Gong G, He Y, Evans AC. Brain connectivity: Gender Makes a Difference. Neuroscientist. 2011;17:575–91. Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.PET CenterHuashan Hospital, Fudan UniversityShanghaiChina
  2. 2.Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy PhysicsChinese Academy of SciencesBeijingChina
  3. 3.Department of Nuclear Medicine, Xinhua HospitalShanghai JiaoTong University School of MedicineShanghaiChina
  4. 4.Shanghai Universal Medical Imaging Diagnostic CenterShanghaiChina
  5. 5.School of Nuclear Science and TechnologyUniversity of Chinese Academy of SciencesBeijingChina
  6. 6.Shanghai Key Laboratory for Molecular ImagingShanghai University of Medicine and Health SciencesShanghaiChina

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