Individual variation in longitudinal postnatal development of the primate brain

  • G. BallEmail author
  • M. L. Seal
Original Article


Quantifying individual variation in postnatal brain development can provide insight into cognitive diversity within a population and the aetiology of common neuropsychiatric and neurodevelopmental disorders. Non-invasive studies of the non-human primate can aid understanding of human brain development, facilitating longitudinal analysis during early postnatal development when comparative human populations are difficult to sample. In this study, we perform analysis of a longitudinal MRI dataset of 32 macaques, each with up to five magnetic resonance imaging (MRI) scans acquired between 3 and 36 months of age. Using nonlinear mixed effects model we derive growth trajectories for whole brain, cortical and subcortical grey matter, cerebral white matter and cerebellar volume. We then test the association between individual variation in postnatal tissue volumes and birth weight. We report nonlinear growth models for all tissue compartments, as well as significant variation in total intracranial volume between individuals. We also demonstrate that regional subcortical grey matter varies both in total volume and rate of change between individuals and is associated with differences in birth weight. This supports evidence that birth weight may act as a marker of subsequent brain development and highlights the importance of longitudinal MRI analysis in developmental studies.


Brain development Macaque Magnetic resonance imaging Nonlinear models 



This research was conducted within the Developmental Imaging research group, Murdoch Children’s Research Institute and the Children’s MRI Centre, Royal Children’s Hospital, Melbourne, Victoria. It was supported by the Murdoch Children’s Research Institute, the Royal Children’s Hospital, Department of Paediatrics, The University of Melbourne and the Victorian Government’s Operational Infrastructure Support Program. The project was generously supported by RCH1000, a unique arm of The Royal Children’s Hospital Foundation devoted to raising funds for research at The Royal Children’s Hospital. We would like to thanks the authors and contributors of the UNC-Wisconsin Rhesus Macaque Neurodevelopment Database. The database was supported by grants from the NIMH (MH901645, MH091645-S1, and MH100031) and the NICHD (HD003352, HD003110, and HD079124). For the CIVM atlas data, all imaging was performed at the Duke Center for In Vivo Microscopy, an NIH/NIBIB National Biomedical Technology Resource Center (P41 EB015897). Other support was provided by NA-MIC Roadmap for Medical Research (U54 EB005149-01), NIMH (R01 MH091645), NICHD (U54 HD079124), and NIA (K01 AG041211). Brain specimens were provided by the Wisconsin National Primate Research Center (P51 OD011106).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

For the macaque data, the research protocol was approved by the local Institutional Animal Care and Use Committee at the University of Wisconsin-Madison. All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. All procedures performed in studies involving animals were in accordance with the ethical standards of the institution or practice at which the studies were conducted.

Supplementary material

429_2019_1829_MOESM1_ESM.pdf (538 kb)
Supplementary material 1 (PDF 538 KB)


  1. Alexander-Bloch AF, Reiss PT, Rapoport J, McAdams H, Giedd JN, Bullmore ET, Gogtay N (2014) Abnormal cortical growth in schizophrenia targets normative modules of synchronized development. Biol Psychiatry 76:438–446CrossRefGoogle Scholar
  2. Amlien IK, Fjell AM, Tamnes CK, Grydeland H, Krogsrud SK, Chaplin TA, Rosa MGP, Walhovd KB (2016) Organizing principles of human cortical development—thickness and area from 4 to 30 years: insights from comparative primate neuroanatomy. Cereb Cortex 26:257–267CrossRefGoogle Scholar
  3. Aubert-Broche B, Fonov VS, García-Lorenzo D, Mouiha A, Guizard N, Coupé P, Eskildsen SF, Collins DL (2013) A new method for structural volume analysis of longitudinal brain MRI data and its application in studying the growth trajectories of anatomical brain structures in childhood. Neuroimage 82:393–402CrossRefGoogle Scholar
  4. Avants BB, Epstein CL, Grossman M, Gee JC (2008) Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 12:26–41CrossRefGoogle Scholar
  5. Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC (2011a) A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54:2033–2044CrossRefGoogle Scholar
  6. Avants BB, Tustison NJ, Wu J, Cook PA, Gee JC (2011b) An open source multivariate framework for n-tissue segmentation with evaluation on public data. Neuroinformatics 9:381–400CrossRefGoogle Scholar
  7. Bakken TE, Miller JA, Ding S-L, Sunkin SM, Smith KA, Ng L, Szafer A, Dalley RA, Royall JJ, Lemon T, Shapouri S, Aiona K, Arnold J, Bennett JL, Bertagnolli D, Bickley K, Boe A, Brouner K, Butler S, Byrnes E, Caldejon S, Carey A, Cate S, Chapin M, Chen J, Dee N, Desta T, Dolbeare TA, Dotson N, Ebbert A, Fulfs E, Gee G, Gilbert TL, Goldy J, Gourley L, Gregor B, Gu G, Hall J, Haradon Z, Haynor DR, Hejazinia N, Hoerder-Suabedissen A, Howard R, Jochim J, Kinnunen M, Kriedberg A, Kuan CL, Lau C, Lee C-K, Lee F, Luong L, Mastan N, May R, Melchor J, Mosqueda N, Mott E, Ngo K, Nyhus J, Oldre A, Olson E, Parente J, Parker PD, Parry S, Pendergraft J, Potekhina L, Reding M, Riley ZL, Roberts T, Rogers B, Roll K, Rosen D, Sandman D, Sarreal M, Shapovalova N, Shi S, Sjoquist N, Sodt AJ, Townsend R, Velasquez L, Wagley U, Wakeman WB, White C, Bennett C, Wu J, Young R, Youngstrom BL, Wohnoutka P, Gibbs RA, Rogers J, Hohmann JG, Hawrylycz MJ, Hevner RF, Molnár Z, Phillips JW, Dang C, Jones AR, Amaral DG, Bernard A, Lein ES (2016) A comprehensive transcriptional map of primate brain development. Nature 535:367–375CrossRefGoogle Scholar
  8. Ball G, Aljabar P, Nongena P, Kennea N, Gonzalez-Cinca N, Falconer S, Chew ATM, Harper N, Wurie J, Rutherford MA, Counsell SJ, Edwards AD (2017) Multimodal image analysis of clinical influences on preterm brain development. Ann Neurol 82:233–246CrossRefGoogle Scholar
  9. Botellero VL, Skranes J, Bjuland KJ, Håberg AK, Lydersen S, Brubakk A-M, Indredavik MS, Martinussen M (2017) A longitudinal study of associations between psychiatric symptoms and disorders and cerebral gray matter volumes in adolescents born very preterm. BMC Pediatrics 17:45CrossRefGoogle Scholar
  10. Bourgeois JP, Rakic P (1993) Changes of synaptic density in the primary visual cortex of the macaque monkey from fetal to adult stage. J Neurosci 13:2801–2820CrossRefGoogle Scholar
  11. Bourgeois JP, Goldman-Rakic PS, Rakic P (1994) Synaptogenesis in the prefrontal cortex of rhesus monkeys. Cereb Cortex 4:78–96CrossRefGoogle Scholar
  12. Calabrese E, Badea A, Coe CL, Lubach GR, Shi Y, Styner MA, Johnson GA (2015) A diffusion tensor MRI atlas of the postmortem rhesus macaque brain. NeuroImage 117:408–416CrossRefGoogle Scholar
  13. Chen X, Errangi B, Li L, Glasser MF, Westlye LT, Fjell AM, Walhovd KB, Hu X, Herndon JG, Preuss TM, Rilling JK (2013) Brain aging in humans, chimpanzees (Pan troglodytes), and rhesus macaques (Macaca mulatta): magnetic resonance imaging studies of macro- and micro-structural changes. Neurobiol Aging 34:2248–2260CrossRefGoogle Scholar
  14. Chiapponi C, Piras F, Fagioli S, Piras F, Caltagirone C, Spalletta G (2013) Age-related brain trajectories in schizophrenia: a systematic review of structural MRI studies. Psychiatry Res 214:83–93CrossRefGoogle Scholar
  15. Courchesne E, Carper R, Akshoomoff N (2003) Evidence of brain overgrowth in the first year of life in autism. JAMA 290:337–344CrossRefGoogle Scholar
  16. Cudeck R (1996) Mixed-effects models in the study of individual differences with repeated measures data. Multivar Behav Res 31:371–403CrossRefGoogle Scholar
  17. de Macedo Rodrigues K, Ben-Avi E, Sliva DD, Choe M-S, Drottar M, Wang R, Fischl B, Grant PE, Zöllei L (2015) A FreeSurfer-compliant consistent manual segmentation of infant brains spanning the 0–2 year age range. Front Hum Neurosci 9:21CrossRefGoogle Scholar
  18. Dekaban S (1978) Changes in brain weights during the span of human life: relation of brain weights to body heights and body weights. Ann Neurol 4:345–356CrossRefGoogle Scholar
  19. Dietrich R, Bradley W, Zaragoza E, Otto R, Taira R, Wilson G, Kangarloo H (1988) MR evaluation of early myelination patterns in normal and developmentally delayed infants. Am J Roentgenol 150:889–896CrossRefGoogle Scholar
  20. Douaud G, Mackay C, Andersson J, James S, Quested D, Ray MK, Connell J, Roberts N, Crow TJ, Matthews PM, Smith S, James A (2009) Schizophrenia delays and alters maturation of the brain in adolescence. Brain 132:2437–2448CrossRefGoogle Scholar
  21. Erp TGM van, Hibar DP, Rasmussen JM, Glahn DC, Pearlson GD, Andreassen OA, Agartz I, Westlye LT, Haukvik UK, Dale AM, Melle I, Hartberg CB, Gruber O, Kraemer B, Zilles D, Donohoe G, Kelly S, McDonald C, Morris DW, Cannon DM, Corvin A, Machielsen MWJ, Koenders L, Haan L de, Veltman DJ, Satterthwaite TD, Wolf DH, Gur RC, Gur RE, Potkin SG, Mathalon DH, Mueller BA, Preda A, Macciardi F, Ehrlich S, Walton E, Hass J, Calhoun VD, Bockholt HJ, Sponheim SR, Shoemaker JM, Haren NEM van, Pol HEH, Ophoff RA, Kahn RS, Roiz-Santiañez R, Crespo-Facorro B, Wang L, Alpert KI, Jönsson EG, Dimitrova R, Bois C, Whalley HC, McIntosh AM, Lawrie SM, Hashimoto R, Thompson PM, Turner JA (2016) Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol Psychiatry 21:547–553CrossRefGoogle Scholar
  22. Foulkes L, Blakemore S-J (2018) Studying individual differences in human adolescent brain development. Nat Neurosci 21:315–323CrossRefGoogle Scholar
  23. Fujikura T, Niemann WH (1967) Birth weight, gestational age, and type of delivery in rhesus monkeys. Am J Obstet Gynecol 97:76–80CrossRefGoogle Scholar
  24. Gennatas ED, Avants BB, Wolf DH, Satterthwaite TD, Ruparel K, Ciric R, Hakonarson H, Gur RE, Gur RC (2017) Age-related effects and sex differences in gray matter density, volume, mass, and cortical thickness from childhood to young adulthood. J Neurosci 37:5065–5073CrossRefGoogle Scholar
  25. Giedd JN, Blumenthal J, Jeffries NO, Castellanos FX, Liu H, Zijdenbos A, Paus T, Evans AC, Rapoport JL (1999) Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci 2:861–863CrossRefGoogle Scholar
  26. Gilmore JH, Shi F, Woolson SL, Knickmeyer RC, Short SJ, Lin W, Zhu H, Hamer RM, Styner M, Shen D (2012) Longitudinal development of cortical and subcortical gray matter from birth to 2 years. Cereb Cortex 22:2478–2485CrossRefGoogle Scholar
  27. Grootel TJV, Meeson A, Munk MHJ, Kourtzi Z, Movshon JA, Logothetis NK, Kiorpes L (2017) Development of visual cortical function in infant macaques: A BOLD fMRI study. PLoS One 12:e0187942CrossRefGoogle Scholar
  28. Harvey PH, Pagel MD (1988) The allometric approach to species differences in brain size. Hum Evol 3:461–472CrossRefGoogle Scholar
  29. Hastie T, Tibshirani R (1986) Generalized additive models. Stat Sci 1:297–310CrossRefGoogle Scholar
  30. Hazlett HC, Gu H, Munsell BC, Kim SH, Styner M, Wolff JJ, Elison JT, Swanson MR, Zhu H, Botteron KN, Collins DL, Constantino JN, Dager SR, Estes AM, Evans AC, Fonov VS, Gerig G, Kostopoulos P, McKinstry RC, Pandey J, Paterson S, Pruett JR, Schultz RT, Shaw DW, Zwaigenbaum L, Piven J, The IBIS, Network (2017) Early brain development in infants at high risk for autism spectrum disorder. Nature 542:348–351CrossRefGoogle Scholar
  31. Herculano-Houzel S, Manger PR, Kaas JH (2014) Brain scaling in mammalian evolution as a consequence of concerted and mosaic changes in numbers of neurons and average neuronal cell size. Front Neuroanat 8:77Google Scholar
  32. Herting MM, Johnson C, Mills KL, Vijayakumar N, Dennison M, Liu C, Goddings A-L, Dahl RE, Sowell ER, Whittle S, Allen NB, Tamnes CK (2018) Development of subcortical volumes across adolescence in males and females: a multisample study of longitudinal changes. NeuroImage 172:194–205CrossRefGoogle Scholar
  33. Holland BA, Haas DK, Norman D, Brant-Zawadzki M, Newton TH (1986) MRI of normal brain maturation. Am J Neuroradiol 7:201–208Google Scholar
  34. Holland D, Chang L, Ernst TM, Curran M, Buchthal SD, Alicata D, Skranes J, Johansen H, Hernandez A, Yamakawa R, Kuperman JM, Dale AM (2014) Structural growth trajectories and rates of change in the first 3 months of infant brain development. JAMA Neurol 71:1266–1274CrossRefGoogle Scholar
  35. Hoogman M, Bralten J, Hibar DP, Mennes M, Zwiers MP, Schweren LSJ, van Hulzen KJE, Medland SE, Shumskaya E, Jahanshad N, Zeeuw P de, Szekely E, Sudre G, Wolfers T, Onnink AMH, Dammers JT, Mostert JC, Vives-Gilabert Y, Kohls G, Oberwelland E, Seitz J, Schulte-Rüther M, Ambrosino S, Doyle AE, Høvik MF, Dramsdahl M, Tamm L, van Erp TGM, Dale A, Schork A, Conzelmann A, Zierhut K, Baur R, McCarthy H, Yoncheva YN, Cubillo A, Chantiluke K, Mehta MA, Paloyelis Y, Hohmann S, Baumeister S, Bramati I, Mattos P, Tovar-Moll F, Douglas P, Banaschewski T, Brandeis D, Kuntsi J, Asherson P, Rubia K, Kelly C, Martino AD, Milham MP, Castellanos FX, Frodl T, Zentis M, Lesch K-P, Reif A, Pauli P, Jernigan TL, Haavik J, Plessen KJ, Lundervold AJ, Hugdahl K, Seidman LJ, Biederman J, Rommelse N, Heslenfeld DJ, Hartman CA, Hoekstra PJ, Oosterlaan J, Polier G von, Konrad K, Vilarroya O, Ramos-Quiroga JA, Soliva JC, Durston S, Buitelaar JK, Faraone SV, Shaw P, Thompson PM, Franke B (2017) Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: a cross-sectional mega-analysis. Lancet Psychiatry 4:310–319CrossRefGoogle Scholar
  36. Hunsaker MR, Scott JA, Bauman MD, Schumann CM, Amaral DG (2014) Postnatal development of the hippocampus in the Rhesus macaque (Macaca mulatta): a longitudinal magnetic resonance imaging study. Hippocampus 24:794–807CrossRefGoogle Scholar
  37. Kanai R, Rees G (2011) The structural basis of inter-individual differences in human behaviour and cognition. Nat Rev Neurosci 12:231–242CrossRefGoogle Scholar
  38. King KM, Littlefield AK, McCabe CJ, Mills KL, Flournoy J, Chassin L (2017) Longitudinal modeling in developmental neuroimaging research: common challenges, and solutions from developmental psychology. Dev Cognit Neurosci 33:54–72CrossRefGoogle Scholar
  39. Kinney HC, Brody BA, Kloman AS, Gilles FH (1988) Sequence of central nervous system myelination in human infancy. II. Patterns of myelination in autopsied infants. J Neuropathol Exp Neurol 47:217–234CrossRefGoogle Scholar
  40. Knickmeyer RC, Styner M, Short SJ, Lubach GR, Kang C, Hamer R, Coe CL, Gilmore JH (2010) Maturational trajectories of cortical brain development through the pubertal transition: unique species and sex differences in the monkey revealed through structural magnetic resonance imaging. Cereb Cortex 20:1053–1063CrossRefGoogle Scholar
  41. Knickmeyer RC, Gouttard S, Kang C, Evans D, Wilber K, Smith JK, Hamer RM, Lin W, Gerig G, Gilmore JH. 2008. A Structural MRI study of human brain development from birth to 2 years. J Neurosci 28:12176–12182Google Scholar
  42. Lampi KM, Lehtonen L, Tran PL, Suominen A, Lehti V, Banerjee PN, Gissler M, Brown AS, Sourander A (2012) Risk of autism spectrum disorders in low birth weight and small for gestational age infants. J Pediatr 161:830–836CrossRefGoogle Scholar
  43. Lebel C, Beaulieu C (2011) Longitudinal development of human brain wiring continues from childhood into adulthood. J Neurosci 31:10937–10947CrossRefGoogle Scholar
  44. Lenroot RK, Gogtay N, Greenstein DK, Wells EM, Wallace GL, Clasen LS, Blumenthal JD, Lerch J, Zijdenbos AP, Evans AC, Thompson PM, Giedd JN (2007) Sexual dimorphism of brain developmental trajectories during childhood and adolescence. NeuroImage 36:1065–1073CrossRefGoogle Scholar
  45. Madhyastha T, Peverill M, Koh N, McCabe C, Flournoy J, Mills K, King K, Pfeifer J, McLaughlin KA (2017) Current methods and limitations for longitudinal fMRI analysis across development. Dev Cognit Neurosci 33:118–128CrossRefGoogle Scholar
  46. Malkova L, Heuer E, Saunders RC (2006) Longitudinal magnetic resonance imaging study of rhesus monkey brain development. Eur J Neurosci 24:3204–3212CrossRefGoogle Scholar
  47. Manjón JV, Coupé P, Buades A, Louis Collins D, Robles M (2012) New methods for MRI denoising based on sparseness and self-similarity. Med Image Anal 16:18–27CrossRefGoogle Scholar
  48. Miller DJ, Duka T, Stimpson CD, Schapiro SJ, Baze WB, McArthur MJ, Fobbs AJ, Sousa AMM, Šestan N, Wildman DE, Lipovich L, Kuzawa CW, Hof PR, Sherwood CC (2012) Prolonged myelination in human neocortical evolution. PNAS 109:16480–16485CrossRefGoogle Scholar
  49. Mills KL, Tamnes CK (2014) Methods and considerations for longitudinal structural brain imaging analysis across development. Dev Cognit Neurosci 9:172–190CrossRefGoogle Scholar
  50. Mills KL, Goddings A-L, Herting MM, Meuwese R, Blakemore S-J, Crone EA, Dahl RE, Güroğlu B, Raznahan A, Sowell ER, Tamnes CK (2016) Structural brain development between childhood and adulthood: convergence across four longitudinal samples. NeuroImage 141:273–281CrossRefGoogle Scholar
  51. Moeskops P, Pluim JPW (2017) Isointense infant brain MRI segmentation with a dilated convolutional neural network. arXiv:1708.02757
  52. Morsing E, Åsard M, Ley D, Stjernqvist K, Maršál K (2011) Cognitive function after intrauterine growth restriction and very preterm birth. Pediatrics 127:e874–e882CrossRefGoogle Scholar
  53. Passingham RE (1985) Rates of brain development in mammals including man. BBE 26:167–175Google Scholar
  54. Payne C, Machado CJ, Bliwise NG, Bachevalier J (2010) Maturation of the hippocampal formation and amygdala in macaca mulatta: a volumetric magnetic resonance imaging study. Hippocampus 20:922–935CrossRefGoogle Scholar
  55. Pfefferbaum A, Mathalon DH, Sullivan EV, Rawles JM, Zipursky RB, Lim KO (1994) A quantitative magnetic resonance imaging study of changes in brain morphology from infancy to late adulthood. Arch Neurol 51:874–887CrossRefGoogle Scholar
  56. Puelles L, Harrison M, Paxinos G, Watson C (2013) A developmental ontology for the mammalian brain based on the prosomeric model. Trends Neurosci 36:570–578CrossRefGoogle Scholar
  57. Raznahan A, Lerch JP, Lee N, Greenstein D, Wallace GL, Stockman M, Clasen L, Shaw PW, Giedd JN (2011) Patterns of coordinated anatomical change in human cortical development: a longitudinal neuroimaging study of maturational coupling. Neuron 72:873–884CrossRefGoogle Scholar
  58. Raznahan A, Greenstein D, Lee NR, Clasen LS, Giedd JN (2012) Prenatal growth in humans and postnatal brain maturation into late adolescence. PNAS 109:11366–11371CrossRefGoogle Scholar
  59. Raznahan A, Shaw PW, Lerch JP, Clasen LS, Greenstein D, Berman R, Pipitone J, Chakravarty MM, Giedd JN (2014) Longitudinal four-dimensional mapping of subcortical anatomy in human development. PNAS 111:1592–1597CrossRefGoogle Scholar
  60. Reuter M, Schmansky NJ, Rosas HD, Fischl B (2012) Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61:1402–1418CrossRefGoogle Scholar
  61. Ritchie SJ, Cox SR, Shen X, Lombardo MV, Reus LM, Alloza C, Harris MA, Alderson HL, Hunter S, Neilson E, Liewald DCM, Auyeung B, Whalley HC, Lawrie SM, Gale CR, Bastin ME, McIntosh AM, Deary IJ (2018) Sex differences in the adult human brain: evidence from 5216 uk biobank participants. Cereb Cortex 28:2959–2975CrossRefGoogle Scholar
  62. Rogosa D, Brandt D, Zimowski M (1982) A growth curve approach to the measurement of change. Psychol Bull 92:726–748CrossRefGoogle Scholar
  63. Sakai T, Mikami A, Suzuki J, Miyabe-Nishiwaki T, Matsui M, Tomonaga M, Hamada Y, Matsuzawa T, Okano H, Oishi K (2017) Developmental trajectory of the corpus callosum from infancy to the juvenile stage: comparative MRI between chimpanzees and humans. PLoS One 12:e0179624CrossRefGoogle Scholar
  64. Scott JA, Grayson D, Fletcher E, Lee A, Bauman MD, Schumann CM, Buonocore MH, Amaral DG (2016) Longitudinal analysis of the developing rhesus monkey brain using magnetic resonance imaging: birth to adulthood. Brain Struct Funct 221:2847–2871CrossRefGoogle Scholar
  65. Seghier ML, Price CJ (2018) Interpreting and utilising intersubject variability in brain function. Trends Cognit Sci 22:517–530CrossRefGoogle Scholar
  66. Seidlitz J, Sponheim C, Glen D, Ye FQ, Saleem KS, Leopold DA, Ungerleider L, Messinger A (2018) A population MRI brain template and analysis tools for the macaque. Neuroimage 170:121–131CrossRefGoogle Scholar
  67. Shaw P, Eckstrand K, Sharp W, Blumenthal J, Lerch JP, Greenstein D, Clasen L, Evans A, Giedd J, Rapoport JL (2007) Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation. Proc Natl Acad Sci USA 104:19649–19654CrossRefGoogle Scholar
  68. Smart IHM, Dehay C, Giroud P, Berland M, Kennedy H (2002) Unique morphological features of the proliferative zones and postmitotic compartments of the neural epithelium giving rise to striate and extrastriate cortex in the monkey. Cereb Cortex 12:37–53CrossRefGoogle Scholar
  69. Soelen ILC van, Brouwer RM, Peper JS, Beijsterveldt TCEM van, Leeuwen M van, Vries LS de, Kahn RS, Pol HEH, Boomsma DI (2010) Effects of gestational age and birth weight on brain volumes in healthy 9 year-old children. J Pediatr 156:896–901CrossRefGoogle Scholar
  70. Sowell ER, Thompson PM, Leonard CM, Welcome SE, Kan E, Toga AW (2004) Longitudinal mapping of cortical thickness and brain growth in normal children. J Neurosci 24:8223–8231CrossRefGoogle Scholar
  71. Sucksdorff M, Lehtonen L, Chudal R, Suominen A, Joelsson P, Gissler M, Sourander A (2015) Preterm birth and poor fetal growth as risk factors of attention-deficit/hyperactivity disorder. Pediatrics 136:e599–e608CrossRefGoogle Scholar
  72. Tamnes CK, Ostby Y, Fjell AM, Westlye LT, Due-Tønnessen P, Walhovd KB (2010) Brain maturation in adolescence and young adulthood: regional age-related changes in cortical thickness and white matter volume and microstructure. Cereb Cortex 20:534–548CrossRefGoogle Scholar
  73. Tamnes CK, Herting MM, Goddings A-L, Meuwese R, Blakemore S-J, Dahl RE, Güroğlu B, Raznahan A, Sowell ER, Crone EA, Mills KL (2017) Development of the cerebral cortex across adolescence: a multisample study of inter-related longitudinal changes in cortical volume, surface area, and thickness. J Neurosci 37:3402–3412CrossRefGoogle Scholar
  74. Tiemeier H, Lenroot RK, Greenstein DK, Tran L, Pierson R, Giedd JN (2010) Cerebellum development during childhood and adolescence: a longitudinal morphometric MRI study. Neuroimage 49:63–70CrossRefGoogle Scholar
  75. Tigges J, Gordon TP, McClure HM, Hall EC, Peters A (1988) Survival rate and life span of rhesus monkeys at the Yerkes regional primate research center. Am J Primatol 15:263–273CrossRefGoogle Scholar
  76. Tolsa CB, Zimine S, Warfield SK, Freschi M, Sancho Rossignol A, Lazeyras F, Hanquinet S, Pfizenmaier M, Huppi PS (2004) Early alteration of structural and functional brain development in premature infants born with intrauterine growth restriction. Pediatr Res 56:132–138CrossRefGoogle Scholar
  77. Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320CrossRefGoogle Scholar
  78. van Rooij D, Anagnostou E, Arango C, Auzias G, Behrmann M, Busatto GF, Calderoni S, Daly E, Deruelle C, Di Martino A, Dinstein I, Duran FLS, Durston S, Ecker C, Fair D, Fedor J, Fitzgerald J, Freitag CM, Gallagher L, Gori I, Haar S, Hoekstra L, Jahanshad N, Jalbrzikowski M, Janssen J, Lerch J, Luna B, Martinho MM, McGrath J, Muratori F, Murphy CM, Murphy DGM, O’Hearn K, Oranje B, Parellada M, Retico A, Rosa P, Rubia K, Shook D, Taylor M, Thompson PM, Tosetti M, Wallace GL, Zhou F, Buitelaar JK (2018) Cortical and subcortical brain morphometry differences between patients with autism spectrum disorder and healthy individuals across the lifespan: results from the ENIGMA ASD working group. Am J Psychiatry 175:359–369CrossRefGoogle Scholar
  79. Walhovd KB, Fjell AM, Brown TT, Kuperman JM, Chung Y, Hagler DJ, Roddey JC, Erhart M, McCabe C, Akshoomoff N, Amaral DG, Bloss CS, Libiger O, Schork NJ, Darst BF, Casey BJ, Chang L, Ernst TM, Frazier J, Gruen JR, Kaufmann WE, Murray SS, Zijl P van, Mostofsky S, Dale AM, for the Pediatric Imaging N (2012) Long-term influence of normal variation in neonatal characteristics on human brain development. PNAS 109:20089–20094CrossRefGoogle Scholar
  80. Walhovd KB, Fjell AM, Giedd J, Dale AM, Brown TT (2017) Through thick and thin: a need to reconcile contradictory results on trajectories in human cortical development. Cereb Cortex 27:1472–1481Google Scholar
  81. Weisenfeld NI, Warfield SK (2009) Automatic segmentation of newborn brain MRI. Neuroimage 47:564–572CrossRefGoogle Scholar
  82. Wierenga L, Langen M, Ambrosino S, van Dijk S, Oranje B, Durston S (2014) Typical development of basal ganglia, hippocampus, amygdala and cerebellum from age 7 to 24. NeuroImage 96:67–72CrossRefGoogle Scholar
  83. Winkler AM, Greve DN, Bjuland KJ, Nichols TE, Sabuncu MR, Håberg AK, Skranes J, Rimol LM (2018) Joint analysis of cortical area and thickness as a replacement for the analysis of the volume of the cerebral cortex. Cereb Cortex 28:738–749CrossRefGoogle Scholar
  84. Wood SN (2003) Thin plate regression splines. J R Stat Soc 65:95–114CrossRefGoogle Scholar
  85. Wood SN (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc 73:3–36CrossRefGoogle Scholar
  86. Wood S (2017) Generalized additive models: an introduction with R. 2nd ed. CRC Press, Boca RatonCrossRefGoogle Scholar
  87. Workman AD, Charvet CJ, Clancy B, Darlington RB, Finlay BL (2013) Modeling transformations of neurodevelopmental sequences across mammalian species. J Neurosci 33:7368–7383CrossRefGoogle Scholar
  88. 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 3–69Google Scholar
  89. Young JT, Shi Y, Niethammer M, Grauer M, Coe CL, Lubach GR, Davis B, Budin F, Knickmeyer RC, Alexander AL, Styner MA (2017) The UNC-Wisconsin Rhesus Macaque neurodevelopment database: a structural MRI and DTI database of early postnatal development. Front Neurosci 11:29CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Developmental ImagingMurdoch Children’s Research InstituteMelbourneAustralia
  2. 2.Department of PaediatricsUniversity of MelbourneMelbourneAustralia

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