Advertisement

Brain Structure and Function

, Volume 224, Issue 2, pp 535–551 | Cite as

Age-dynamic networks and functional correlation for early white matter myelination

  • Xiongtao DaiEmail author
  • Hans-Georg Müller
  • Jane-Ling Wang
  • Sean C. L. Deoni
Original Article

Abstract

The maturation of the myelinated white matter throughout childhood is a critical developmental process that underlies emerging connectivity and brain function. In response to genetic influences and neuronal activities, myelination helps establish the mature neural networks that support cognitive and behavioral skills. The emergence and refinement of brain networks, traditionally investigated using functional imaging data, can also be interrogated using longitudinal structural imaging data. However, few studies of structural network development throughout infancy and early childhood have been presented, likely owing to the sparse and irregular nature of most longitudinal neuroimaging data, which complicates dynamic analysis. Here, we overcome this limitation and investigate through concurrent correlation the co-development of white matter myelination and volume, and structural network development of white matter myelination between brain regions as a function of age, using statistically well-supported methods. We show that the concurrent correlation of white matter myelination and volume is overall positive and reaches a peak at 580 days. Brain regions are found to differ in overall magnitudes and patterns of time-varying association throughout early childhood. We introduce time-dynamic developmental networks based on temporal similarity of association patterns in the levels of myelination across brain regions. These networks reflect groups of brain regions that share similar patterns of evolving intra-regional connectivity, as evidenced by levels of myelination, are biologically interpretable and provide novel visualizations of brain development. Comparing the constructed networks between different maternal education groups, we found that children with higher and lower maternal education differ significantly in the overall magnitude of the time-dynamic correlations.

Keywords

Whole brain MRI Myelination Developmental network Concurrent correlation structure 

Notes

Acknowledgements

This work was supported by the National Science Foundation (DMS-1407852, DMS-1512975), the National Institutes of Mental Health (R01 MH087510), and the Bill and Melinda Gates Foundation (OPP11002016).

Supplementary material

429_2018_1785_MOESM1_ESM.docx (2.4 mb)
Supplementary material 1 (DOCX 2459 KB)

References

  1. Bali JL, Boente G, Tyler DE, Wang JL (2011) Robust functional principal components: a projection-pursuit approach. Ann Stat 39(6):2852–2882Google Scholar
  2. Beckmann CF (2012) Modelling with independent components. NeuroImage 62(2):891–901Google Scholar
  3. Bornstein MH, Hahn CS, Suwalsky JTD, Haynes OM (2003) Socioeconomic status, parenting, and child development: the Hollingshead Four-Factor Index of Social Status and the Socioeconomic Index of Occupations. In: Bornstein MH, Bradley RH (eds) Socioeconomic status, parenting, and child development. Lawrence Erlbaum Associates Publishers, Mahwah, pp 29–82Google Scholar
  4. Brett M (1999) The MNI brain and the Talairach atlas, Technical reportGoogle Scholar
  5. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186–198Google Scholar
  6. Casey BJ, Giedd JN, Thomas KM (2000) Structural and functional brain development and its relation to cognitive development. Biol Psychol 54(1–3):241–257Google Scholar
  7. Castro PE, Lawton WH, Sylvestre EA (1986) Principal modes of variation for processes with continuous sample curves. Technometrics 28(4):329–337Google Scholar
  8. Chevalier N, Kurth S, Doucette MR, Wiseheart M (2015) Myelination is associated with processing speed in early childhood: preliminary insights. PLoS ONE 10(10):e0139897Google Scholar
  9. Croux C, Ruiz-Gazen A (2005) High breakdown estimators for principal components: the projection-pursuit approach revisited. J Multivar Anal 95(1):206–226Google Scholar
  10. Dai X, Hadjipantelis PZ, Han K, Ji H, Lin SC, Müller HG, Wang JL (2018) fdapace: functional data analysis and empirical dynamics. R package version 0.4.0. https://cran.r-project.org/package=fdapace. Accessed 30 Oct 2018
  11. Dean DC III, Dirks H, O’Muircheartaigh J, Walker L, Jerskey BA, Lehman K et al (2014a) Pediatric neuroimaging using magnetic resonance imaging during non-sedated sleep. Pediatr Radiol 44(1):64–72Google Scholar
  12. Dean DC III, O’Muircheartaigh J, Dirks H, Waskiewicz N, Lehman K, Walker L et al (2014b) Modeling healthy male white matter and myelin development: 3 through 60 months of age. NeuroImage 84:742–752Google Scholar
  13. Dean DC, O’Muircheartaigh J, Dirks H, Waskiewicz N, Walker L, Doernberg E, Piryatinsky I, Deoni SC (2015) Characterizing longitudinal white matter development during early childhood. Brain Struct Funct 220(4):1921–1933Google Scholar
  14. Dementieva YA, Vance DD, Donnelly SL, Elston LA, Wolpert CM, Ravan SA, DeLong GR, Abramson RK, Wright HH, Cuccaro ML (2005) Accelerated head growth in early development of individuals with autism. Pediatr Neurol 32(2):102–108Google Scholar
  15. Deoni SCL (2011) Correction of main and transmit magnetic field (B0 and B1) inhomogeneity effects in multicomponent-driven equilibrium single-pulse observation of T1 and T2. Magn Reson Med 65(4):1021–1035Google Scholar
  16. Deoni SCL, Dean DC III, O’Muircheartaigh J, Dirks H, Jerskey BA (2012) Investigating white matter development in infancy and early childhood using myelin water faction and relaxation time mapping. NeuroImage 63(3):1038–1053Google Scholar
  17. Deoni SC, Dean DC III, Piryatinsky I, O’Muircheartaigh J, Waskiewicz N, Lehman K, Han M, Dirks H (2013a) Breastfeeding and early white matter development: a cross-sectional study. NeuroImage 82:77–86Google Scholar
  18. Deoni SCL, Matthews L, Kolind SH (2013b) One component? Two components? Three? The effect of including a nonexchanging “free” water component in multicomponent driven equilibrium single pulse observation of T1 and T2. Magn Reson Med 70(1):147–154 (PMCID: 3711852) Google Scholar
  19. Deoni SCL, Rutt BK, Arun T (2008) Gleaning multicomponent T1 and T2 information from steady-state imaging data. Magn Reson Med 60(6):1372–1387Google Scholar
  20. Deoni SCL, O’Muircheartaigh J, Elison JT, Walker L, Doernberg E, Waskiewicz N, Dirks H, Piryatinsky I, Dean DC III, Jumbe NL (2016) White matter maturation profiles through early childhood predict general cognitive ability. Brain Struct Funct 221(2):1189–1203Google Scholar
  21. Durston S, Casey BJ (2006) What have we learned about cognitive development from neuroimaging? Neuropsychologia 44(11):2149–2157Google Scholar
  22. Fair DA, Dosenbach NUF, Church JA, Cohen AL, Brahmbhatt S, Miezin FM et al (2007) Development of distinct control networks through segregation and integration. PNAS 104(33):13507–13512 (PMCID: PMC1940033) Google Scholar
  23. Fair DA, Nigg JT, Iyer S, Bathula D, Mills KL, Dosenbach NUF et al (2012) Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data. Front Syst Neurosci 6:80 (PMCID: PMC3563110) Google Scholar
  24. Fan J, Gijbels I (1996) Local polynomial modelling and its applications: monographs on statistics and applied probability, vol 66. CRC Press, Boca RatonGoogle Scholar
  25. Fan J, Yao Q (1998) Efficient estimation of conditional variance functions in stochastic regression. Biometrika 1:645–660Google Scholar
  26. Fornito A, Zalesky A, Pantelis C, Bullmore ET (2012) Schizophrenia, neuroimaging and connectomics. NeuroImage 62(4):2296–2314Google Scholar
  27. Gao W, Alcauter S, Elton A, Hernandez-Castillo CR, Smith JK, Ramirez J et al (2015) Functional network development during the first year: relative sequence and socioeconomic correlations. Cereb Cortex 25(9):2919–2928 (PMCID: PMC4537436) Google Scholar
  28. Grenander U (1950) Stochastic processes and statistical inference. Arkiv för matematik 1(3):195–277Google Scholar
  29. Hackman DA, Farah MJ (2009) Socioeconomic status and the developing brain. Trends Cogn Sci 13(2):65–73 (PMCID: PMC3575682) Google Scholar
  30. Hair NL, Hanson JL, Wolfe BL, Pollak SD (2015) Association of child poverty, brain development, and academic achievement. JAMA Pediatr 169(9):822–829 (PMCID: PMC4687959) Google Scholar
  31. Hensch TK, Bilimoria PM (2012) Re-opening windows: manipulating critical periods for brain development. Cerebrum 2012:11 (PMCID: PMC3574806) Google Scholar
  32. Hoeffding W (1940) Masstabinvariante korrelationstheorie, vol 5. Schriften Des Mathematischen Instituts Und Des Instituts Für Angewandte Mathematik Der Universität Berlin, Berlin, pp 181–233Google Scholar
  33. Hollingshead AB (1975) Four factor index of social status, Technical reportGoogle Scholar
  34. Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17(2):825–841Google Scholar
  35. Johnson MH (2001) Functional brain development in humans. Nat Rev Neurosci 2:475–483Google Scholar
  36. Jones MC, Rice JA (1992) Displaying the important features of large collections of similar curves. Am Stat 46(2):140–145Google Scholar
  37. Kolind SH, Matthews L, Johansen-Berg H, Leite MI, Williams SCR, Deoni S, Palace J (2012) Myelin water imaging reflects clinical variability in multiple sclerosis. NeuroImage 60:263–270Google Scholar
  38. Lewis JD, Theilmann RJ, Townsend J, Evans AC (2013) Network efficiency in autism spectrum disorder and its relation to brain overgrowth. Front Hum Neurosci 7:845Google Scholar
  39. MacKay AL, Vavasour IM, Rouscher A, Kolind SH, Madler B, Moore GR, Traboulsee AL, Li DK, Laule C (2009) MR relaxation in multiple sclerosis. Neuroimaging Clin 19:1–26Google Scholar
  40. Marín O (2016) Developmental timing and critical windows for the treatment of psychiatric disorders. Nat Med 22(11):1229–1238Google Scholar
  41. Mori S, Oishi K, Jiang H, Jiang L, Li X, Akhter K et al (2008) Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. NeuroImage 40:570–582Google Scholar
  42. Mullen EM (1995) Mullen scales of early learning, Technical reportGoogle Scholar
  43. Müller HG (1987) Weighted local regression and kernel methods for nonparametric curve fitting. J Am Stat Assoc 82(397):231–238Google Scholar
  44. Müller HG (2005) Functional modelling and classification of longitudinal data. Scand J Stat 32(2):223–240Google Scholar
  45. Nagy Z, Westerberg H, Klingberg T (2004) Maturation of white matter is associated with the development of cognitive functions during childhood. J Cogn Neurosci 16(7):1227–1233 (PMCID: 15453975) Google Scholar
  46. Noble KG, Norman MF, Farah MJ (2005) Neurocognitive correlates of socioeconomic status in kindergarten children. Dev Sci 8:74–87Google Scholar
  47. Noble KG, Houston SM, Brito NH, Bartsch H, Kan E, Kuperman JM et al (2015) Family income, parental education and brain structure in children and adolescents. Nat Neurosci 18(5):773–778 (PMCID: PMC4414816) Google Scholar
  48. O’Brien JS, Sampson EL (1965) Lipid composition of the normal human brain: gray matter, white matter, and myelin. J Lipid Res 6(4):537–544Google Scholar
  49. O’Muircheartaigh J, Dean DC, Ginestet CE, Walker L, Waskiewicz N, Lehman K, Dirks H, Piryatinsky I, Deoni SC (2014) White matter development and early cognition in babies and toddlers. Hum Brain Mapp 35(9):4475–4487Google Scholar
  50. Paus T (2010) Growth of white matter in the adolescent brain: Myelin or axon? Brain Cogn 72(1):26–35Google Scholar
  51. Petersen A, Deoni S, Müller HG (2018) Fréchet estimation of time-varying covariance matrices from sparse data, with application to the regional co-evolution of myelination in the developing brain. Ann Appl Stat (to appear). https://www.imstat.org/journals-and-publications/annals-of-applied-statistics/annals-of-applied-statisticsnext-issues/
  52. Ramsay JO, Silverman BW (2005) Functional data analysis, 2nd edn. Springer, BerlinGoogle Scholar
  53. Raschle N, Zuk J, Ortiz-Mantilla S, Sliva DD, Franceschi A, Grant PE, Benasich AA, Gaab N (2012) Pediatric neuroimaging in early childhood and infancy: challenges and practical guidelines. Ann N Y Acad Sci 1252(1):43–50Google Scholar
  54. Shaw P, Greenstein D, Lerch J, Clasen L, Lenroot R, Gogtay N et al (2006) Intellectual ability and cortical development in children and adolescents. Nature 440(7084):676–679 (PMCID: 16572172) Google Scholar
  55. Shaw P, Lalonde F, Lepage C, Rabin C, Eckstrand K, Sharp W et al (2009) Development of cortical asymmetry in typically developing children and its disruption in attention-deficit/hyperactivity disorder. Arch Gen Psychiatry 66(8):888–896. PMCID: PMC2948210Google Scholar
  56. Silverman BW (1986) Density estimation for statistics and data analysis. Chapman & Hall, LondonGoogle Scholar
  57. Sirin SR (2005) Socioeconomic status and academic achievement: a meta-analytic review of research. Rev Educ Res 75:417–453Google Scholar
  58. Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17(3):143–155Google Scholar
  59. Smith SM, Vidaurre D, Beckmann CF, Glasser MF, Jenkinson M, Miller KL et al (2013) Functional connectomics from resting-state fMRI. Trends Cogn Sci 17(12):666–682 (PMCID: PMC4004765) Google Scholar
  60. Supekar K, Uddin LQ, Prater K, Amin H, Greicius MD (2010) Development of functional and structural connectivity within the default mode network in young children. NeuroImage 52:290–301Google Scholar
  61. Uddin LQ, Supekar K, Menon V (2010) Typical and atypical development of functional human brain networks: insights from resting-state FMRI. Front Syst Neurosci 4:21 (PMCID: PMC2889680) Google Scholar
  62. van der Knaap MS, Valk J, Bakker CJ, Schooneveld M, Faber JA, Willemse J et al (1991) Myelination as an expression of the functional maturity of the brain. Dev Med Child Neurol 33(10):849–857 (PMCID: 1743407) Google Scholar
  63. Vogel AC, Power JD, Petersen SE, Schlaggar BL (2010) Development of the brain’s functional network architecture. Neuropsychol Rev 20:362–375Google Scholar
  64. Wang J, Zuo X, He Y (2010) Graph-based network analysis of resting-state functional MRI. Front Syst Neurosci 4:16Google Scholar
  65. Wang JL, Chiou JM, Müller HG (2016) Review of functional data analysis. Annu Rev Stat Appl 3:257–295Google Scholar
  66. Wolff JJ, Gu H, Gerig G, Elison JT, Styner M, Gouttard S et al (2012) Differences in white matter fiber tract development present from 6 to 24 months in infants with autism. Am J Psychiatry 169(6):589–600 (PMCID: 3377782) Google Scholar
  67. Wood TC, Simmons C, Hurley SA, Wernon AC, Torres J, Dell’Acqua F, Williams SCR, Cash D (2016) Whole brain ex-vivo quantitative MRI of the cuprizone mouse model. PeerJ 4:e2632Google Scholar
  68. Yakovlev PI, Lecours AR (1967) The myelogenetic cycles of regional maturation of the brain. In: Minkowski A (ed) Regional development of the brain in early life. Blackwell, Oxford, pp 3–70Google Scholar
  69. Zatorre RJ, Fields RD, Johansen-Berg H (2012) Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat Neurosci Nat Publ Group 15(4):528–536 (PMCID: PMC3660656) Google Scholar
  70. Zhang X, Wang JL (2016) From sparse to dense functional data and beyond. Ann Stat 44(5):2281–2321Google Scholar
  71. Zhang Y, Brady M, Smith S (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 20(1):45–57Google Scholar
  72. Zhang H, Meltzer P, Davis S (2013) RCircos: an R package for Circos 2D track plots. BMC Bioinform 14(1):244Google Scholar
  73. Zhou Y, Lin SC, Wang JL (2018) Local and global temporal correlations for longitudinal data. J Multivar Anal 167(2018):1–14Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of StatisticsIowa State UniversityAmesUSA
  2. 2.Department of StatisticsUniversity of California DavisDavisUSA
  3. 3.Advanced Baby Imaging LabBrown University School of EngineeringProvidenceUSA

Personalised recommendations