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Longitudinal growth of the basal ganglia and thalamus in very preterm children

  • Wai Yen LohEmail author
  • Peter J. Anderson
  • Jeanie L. Y. Cheong
  • Alicia J. Spittle
  • Jian Chen
  • Katherine J. Lee
  • Charlotte Molesworth
  • Terrie E. Inder
  • Alan Connelly
  • Lex W. Doyle
  • Deanne K. Thompson
ORIGINAL RESEARCH
  • 43 Downloads

Abstract

The impact of very preterm (VP) birth on the development of individual basal ganglia nuclei and the thalamus during childhood remains unclear. We first aimed to compare (1a) the volumes of individual basal ganglia nuclei (nucleus accumbens, caudate nucleus, pallidum, putamen) and the thalamus at age 7 years, and (1b) their volumetric change from infancy to 7 years, in VP children with term-born children. Secondly, we aimed to (2a) determine whether basal ganglia and thalamic volumes at 7 years, or (2b) basal ganglia and thalamic growth rates from infancy to 7 years were associated with neurodevelopmental outcomes at 7 years, and whether these associations differed between the VP and term-born children. One hundred and fifty-four VP (<30 weeks’ gestational age or birth weight < 1250 g) and 35 term-born children had useable magnetic resonance imaging (MRI) scans that could be analyzed at 7 years. Of these, 149 VP and 30 term-born infants also had useable MRI scans at term-equivalent age. Volumes of the individual basal ganglia nuclei and the thalamus were automatically generated from the MRI scans. Compared with the term-born group, the VP group had smaller basal ganglia and thalamic volumes at 7 years and slower growth rates from birth to 7 years. After controlling for overall brain size, VP children still had smaller thalamic volumes but the deep grey matter volume growth rates from birth to 7 years were similar between groups. Reduced basal ganglia and thalamic volumes and slower growth rates in the VP group were associated with poorer cognition, academic achievement and motor function at 7 years. After controlling for overall brain size, the nucleus accumbens and pallidum were the deep grey matter structures most strongly associated with 7-year neurodevelopmental outcomes. In conclusion, basal ganglia and thalamic growth is delayed during early childhood in VP children, with delayed development contributing to poorer functional outcomes.

Keywords

Very preterm Basal ganglia Thalamus Neurodevelopment 

Abbreviations

CNAcc

Caudate nucleus/Nucleus accumbens complex

IQ

Intelligence Quotient

M

Mean

MABC-2

Movement Assessment Battery for Children-2

MANTiS

Morphologically Adaptive Neonatal Tissue Segmentation

MRI

Magnetic Resonance Imaging

PSST

Pediatric Subcortical Segmentation Technique

SD

Standard Deviation

SDQ

Strengths and Difficulties Questionnaire

TBV

Total Brain Volume

VIBeS

Victorian Infant Brain Studies

VP

Very Preterm

Notes

Acknowledgements

We are grateful for the help and support of the Victorian Infant Brain Studies (VIBeS) and Developmental Imaging groups, as well as the Melbourne Children’s MRI Centre at the Murdoch Childrens Research institute. We also thank the families and children who participated in this study.

Funding

This study was supported by Australia’s National Health & Medical Research Council: Centre for Clinical Research Excellence 546519 (L.W.D. and P.J.A.); Centre for Research Excellence 1060733 (L.W.D., P.J.A., J.L.Y.C., D.K.T., A.J.S. and W.Y.L.); Project Grant 491209 (P.J.A., L.W.D., T.E.I. and J.L.Y.C.; Senior Research Fellowship 628371 & 1081288 (P.J.A.); Career Development Fellowships 1108714 (A.J.S.), 1085754 (D.K.T.), 1053609 to (K.J.L.); Early Career Fellowship 1053787 (J.L.Y.C.). This study was also supported by the National Institutes of Health (HD058056), the Victorian Government’s Operational Infrastructure Support Program, and The Royal Children’s Hospital Foundation.

Compliance with ethical standards

Conflict of interest

Wai Yen Loh, Peter J Anderson, Jeanie LY Cheong, Alicia J Spittle, Jian Chen, Katherine J Lee, Charlotte Molesworth, Terrie E Inder, Alan Connelly, Lex W Doyle and Deanne K Thompson 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 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Abernethy, L. J., Palaniappan, M., & Cooke, R. W. (2002). Quantitative magnetic resonance imaging of the brain in survivors of very low birth weight. Arch Dis Child, 87(4), 279–283.CrossRefGoogle Scholar
  2. Abernethy, L. J., Cooke, R. W. I., & Foulder-Hughes, L. (2004). Caudate and hippocampal volumes, intelligence, and motor impairment in 7-year-old children who were born preterm. Pediatr Res, 55(5), 884–893.CrossRefGoogle Scholar
  3. Allin, M., Henderson, M., Suckling, J., Nosarti, C., Rushe, T., Fearon, P., et al. (2004). Effects of very low birthweight on brain structure in adulthood. Developmental Medicine and Child Neurology, 46(1), 46–53.CrossRefGoogle Scholar
  4. Anderson, P. (2002). Assessment and Development of Executive Function (EF) During Childhood. Child Neuropsychology, 8(2), 71–82.  https://doi.org/10.1076/chin.8.2.71.8724.CrossRefGoogle Scholar
  5. Anderson, P., Anderson, V., & Lajoie, G. (1996). The Tower of London Test: Validation and standardization for pediatric populations. Clinical Neuropsychologist, 10(1), 54–65.  https://doi.org/10.1080/13854049608406663.CrossRefGoogle Scholar
  6. Arsalidou, M., Duerden, E. G., & Taylor, M. J. (2013). The Centre of the Brain: Topographical Model of Motor, Cognitive, Affective, and Somatosensory Functions of the Basal Ganglia. Human Brain Mapping, 34(11), 3031–3054.  https://doi.org/10.1002/hbm.22124.CrossRefGoogle Scholar
  7. Ball, G., Boardman, J. P., Rueckert, D., Aljabar, P., Arichi, T., Merchant, N., et al. (2012). The Effect of Preterm Birth on Thalamic and Cortical Development. Cerebral Cortex, 22(5), 1016–1024.  https://doi.org/10.1093/cercor/bhr176.CrossRefGoogle Scholar
  8. Beare, R. J., Chen, J., Kelly, C. E., Alexopoulos, D., Smyser, C. D., Rogers, C. E., et al. (2016). Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation. Front Neuroinform, 10, 12.  https://doi.org/10.3389/fninf.2016.00012.CrossRefGoogle Scholar
  9. Benjamini, Y., Drai, D., Elmer, G., Kafkafi, N., & Golani, I. (2001). Controlling the false discovery rate in behavior genetics research. Behavioural Brain Research, 125(1–2), 279–284.CrossRefGoogle Scholar
  10. Bigler, E. D. (2015). Structural Image Analysis of the Brain in Neuropsychology Using Magnetic Resonance Imaging (MRI) Techniques. Neuropsychol Rev, 25(3), 224–249.  https://doi.org/10.1007/s11065-015-9290-0.CrossRefGoogle Scholar
  11. Bjuland, K. J., Rimol, L. M., Lohaugen, G. C., & Skranes, J. (2014). Brain volumes and cognitive function in very-low-birth-weight (VLBW) young adults. European Journal of Paediatric Neurology, 18(5), 578–590.  https://doi.org/10.1016/j.ejpn.2014.04.004.CrossRefGoogle Scholar
  12. Boardman, J. P., Counsell, S. J., Rueckert, D., Kapellou, O., Bhatia, K. K., Aljabar, P., et al. (2006). Abnormal deep grey matter development following preterm birth detected using deformation-based morphometry. Neuroimage, 32(1), 70–78.  https://doi.org/10.1016/j.neuroimage.2006.03.029.CrossRefGoogle Scholar
  13. Bromberg-Martin, E. S., Matsumoto, M., & Hikosaka, O. (2010). Dopamine in motivational control: rewarding, aversive, and alerting. Neuron, 68(5), 815–834.  https://doi.org/10.1016/j.neuron.2010.11.022.CrossRefGoogle Scholar
  14. Cheong, J. L., Anderson, P. J., Roberts, G., Burnett, A. C., Lee, K. J., Thompson, D. K., et al. (2013). Contribution of brain size to IQ and educational underperformance in extremely preterm adolescents. PLoS One, 8(10), e77475.  https://doi.org/10.1371/journal.pone.0077475.CrossRefGoogle Scholar
  15. Draganski, B., Kherif, F., Klöppel, S., Cook, P. A., Alexander, D. C., Parker, G. J. M., et al. (2008). Evidence for Segregated and Integrative Connectivity Patterns in the Human Basal Ganglia. Journal of Neuroscience, 28(28), 7143–7152.  https://doi.org/10.1523/jneurosci.1486-08.2008.CrossRefGoogle Scholar
  16. Fischi-Gomez, E., Vasung, L., Meskaldji, D. E., Lazeyras, F., Borradori-Tolsa, C., Hagmann, P., et al. (2015). Structural Brain Connectivity in School-Age Preterm Infants Provides Evidence for Impaired Networks Relevant for Higher Order Cognitive Skills and Social Cognition. Cerebral Cortex, 25(9), 2793–2805.  https://doi.org/10.1093/cercor/bhu073.CrossRefGoogle Scholar
  17. Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774–781.  https://doi.org/10.1016/j.neuroimage.2012.01.021.CrossRefGoogle Scholar
  18. Gilmore, J. H., Shi, F., Woolson, S. L., Knickmeyer, R. C., Short, S. J., Lin, W., et al. (2012). Longitudinal development of cortical and subcortical gray matter from birth to 2 years. Cerebral Cortex, 22(11), 2478–2485.  https://doi.org/10.1093/cercor/bhr327.CrossRefGoogle Scholar
  19. Goddings, A.-L., Mills, K. L., Clasen, L. S., Giedd, J. N., Viner, R. M., & Blakemore, S.-J. (2014). The influence of puberty on subcortical brain development. Neuroimage, 88(0), 242–251.  https://doi.org/10.1016/j.neuroimage.2013.09.073.CrossRefGoogle Scholar
  20. Goodman, R. (1997). The Strengths and Difficulties Questionnaire: a research note. J Child Psychol Psychiatry, 38(5), 581–586.CrossRefGoogle Scholar
  21. Henderson, S. E., Sugden, D. A., & Barnett, A. L. (2007). Movement Assessment Battery for Children - second edition (Movement ABC-2). London: The Psychological Corporation.Google Scholar
  22. Holland, D., Chang, L., Ernst, T. M., Curran, M., Buchthal, S. D., Alicata, D., et al. (2014). Structural growth trajectories and rates of change in the first 3 months of infant brain development. JAMA Neurol, 71(10), 1266–1274.  https://doi.org/10.1001/jamaneurol.2014.1638.CrossRefGoogle Scholar
  23. Hollander, E., Anagnostou, E., Chaplin, W., Esposito, K., Haznedar, M. M., Licalzi, E., et al. (2005). Striatal volume on magnetic resonance imaging and repetitive behaviors in autism. Biological Psychiatry, 58(3), 226–232.  https://doi.org/10.1016/j.biopsych.2005.03.040.CrossRefGoogle Scholar
  24. Hoon, A. H., Jr., Stashinko, E. E., Nagae, L. M., Lin, D. D. M., Keller, J., Bastian, A., et al. (2009). Sensory and motor deficits in children with cerebral palsy born preterm correlate with diffusion tensor imaging abnormalities in thalamocortical pathways. Developmental Medicine and Child Neurology, 51(9), 697–704,  https://doi.org/10.1111/j.1469-8749.2009.03306.x.
  25. Inder, T. E., Warfield, S. K., Wang, H., Hüppi, P. S., & Volpe, J. J. (2005). Abnormal cerebral structure is present at term in premature infants. [article]. Pediatrics, 115(2), 286–294.  https://doi.org/10.1542/peds.2004-0326.CrossRefGoogle Scholar
  26. Kesler, S. R., Ment, L. R., Vohr, B., Pajot, S. K., Schneider, K. C., Katz, K. H., et al. (2004). Volumetric analysis of regional cerebral development in preterm children. Pediatric Neurology, 31(5), 318–325.  https://doi.org/10.1016/j.pediatrneurol.2004.06.008.CrossRefGoogle Scholar
  27. Kostovic, I., & Judas, M. (2010). The development of the subplate and thalamocortical connections in the human foetal brain. Acta Paediatrica, 99(8), 1119–1127.  https://doi.org/10.1111/j.1651-2227.2010.01811.x.CrossRefGoogle Scholar
  28. Lenroot, R. K., & Giedd, J. N. (2006). Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging. Neuroscience and Biobehavioral Reviews, 30(6), 718–729.  https://doi.org/10.1016/j.neubiorev.2006.06.001.CrossRefGoogle Scholar
  29. Lenroot, R. K., Gogtay, N., Greenstein, D. K., Wells, E. M., Wallace, G. L., Clasen, L. S., et al. (2007). Sexual dimorphism of brain developmental trajectories during childhood and adolescence. Neuroimage, 36(4), 1065–1073.  https://doi.org/10.1016/j.neuroimage.2007.03.053.CrossRefGoogle Scholar
  30. Ligam, P., Haynes, R. L., Folkerth, R. D., Liu, L., Yang, M., Volpe, J. J., et al. (2009). Thalamic damage in periventricular leukomalacia: Novel pathologic observations relevant to cognitive deficits in survivors of prematurity. Pediatric Research, 65(5), 524–529.  https://doi.org/10.1203/PDR.0b013e3181998baf.CrossRefGoogle Scholar
  31. Lin, Y., Okumura, A., Hayakawa, F., Kato, T., Kuno, K., & Watanabe, K. (2001). Quantitative evaluation of thalami and basal ganglia in infants with periventricular leukomalacia. Developmental Medicine and Child Neurology, 43(7), 481–485.  https://doi.org/10.1111/j.1469-8749.2001.tb00747.x.CrossRefGoogle Scholar
  32. Lin, H. Y., Ni, H. C., Lai, M. C., Tseng, W. Y., & Gau, S. S. (2015). Regional brain volume differences between males with and without autism spectrum disorder are highly age-dependent. Molecular Autism, 6, 29.  https://doi.org/10.1186/s13229-015-0022-3.CrossRefGoogle Scholar
  33. Loh, W. Y., Connelly, A., Cheong, J. L., Spittle, A. J., Chen, J., Adamson, C., et al. (2016). A new MRI-based pediatric subcortical segmentation technique (PSST). Neuroinformatics, 14(1), 69–81.  https://doi.org/10.1007/s12021-015-9279-0.CrossRefGoogle Scholar
  34. Loh, W. Y., Anderson, P. J., Cheong, J. L. Y., Spittle, A. J., Chen, J., Lee, K. J., et al. (2017). Neonatal basal ganglia and thalamic volumes: Very preterm birth and 7-year neurodevelopmental outcomes. Pediatric Research.  https://doi.org/10.1038/pr.2017.161.
  35. Makropoulos, A., Aljabar, P., Wright, R., Huning, B., Merchant, N., Arichi, T., et al. (2016). Regional growth and atlasing of the developing human brain. NeuroImage, 125, 456–478.  https://doi.org/10.1016/j.neuroimage.2015.10.047.CrossRefGoogle Scholar
  36. Manly, T., Anderson, V., Nimmo-Smith, I., Turner, A., Watson, P., & Robertson, I. H. (2001). The differential assessment of children's attention: The test of everyday attention for children (TEA-Ch), normative sample and ADHD performance. Journal of Child Psychology and Psychiatry and Allied Disciplines, 42(8), 1065–1081.CrossRefGoogle Scholar
  37. McClendon, E., Chen, K., Gong, X., Sharifnia, E., Hagen, M., Cai, V., et al. (2014). Prenatal cerebral ischemia triggers dysmaturation of caudate projection neurons. Annals of Neurology, 75(4), 508–524.  https://doi.org/10.1002/ana.24100.CrossRefGoogle Scholar
  38. Ment, L. R., Kesler, S., Vohr, B., Katz, K. H., Baumgartner, H., Schneider, K. C., et al. (2009). Longitudinal brain volume changes in preterm and term control subjects during late childhood and adolescence. Pediatrics, 123(2), 503–511.  https://doi.org/10.1542/peds.2008-0025.CrossRefGoogle Scholar
  39. Monson, B. B., Anderson, P. J., Matthews, L. G., Neil, J. J., Kapur, K., Cheong, J. L., et al. (2016). Examination of the pattern of growth of cerebral tissue volumes from hospital discharge to early childhood in very preterm infants. JAMA Pediatrics, 170(8), 772–779.  https://doi.org/10.1001/jamapediatrics.2016.0781.CrossRefGoogle Scholar
  40. Narvacan, K., Treit, S., Camicioli, R., Martin, W., & Beaulieu, C. (2017). Evolution of deep gray matter volume across the human lifespan. Human Brain Mapping.  https://doi.org/10.1002/hbm.23604.
  41. Nelson, A. B., & Kreitzer, A. C. (2014). Reassessing models of basal ganglia function and dysfunction. Annual Review of Neuroscience, 37(1), 117–135.  https://doi.org/10.1146/annurev-neuro-071013-013916.CrossRefGoogle Scholar
  42. Peterson, B. S., Vohr, B., Staib, L. H., Cannistraci, C. J., Dolberg, A., Schneider, K. C., et al. (2000). Regional brain volume abnormalities and long-term cognitive outcome in preterm infants. JAMA: The Journal of the American Medical Association, 284(15), 1939–1947.CrossRefGoogle Scholar
  43. Pickering, S., & Gathercole, S. (2001). Working memory test battery for children-manual. London: The Psychological Corporation.Google Scholar
  44. Pietschnig, J., Penke, L., Wicherts, J. M., Zeiler, M., & Voracek, M. (2015). Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean? Neuroscience and Biobehavioral Reviews, 57, 411–432.  https://doi.org/10.1016/j.neubiorev.2015.09.017.CrossRefGoogle Scholar
  45. Qiu, A., Crocetti, D., Adler, M., Mahone, E. M., Denckla, M. B., Miller, M. I., et al. (2009). Basal ganglia volume and shape in children with attention deficit hyperactivity disorder. American Journal of Psychiatry, 166(1), 74–82.  https://doi.org/10.1176/appi.ajp.2008.08030426.CrossRefGoogle Scholar
  46. Raznahan, A., Shaw, P. W., Lerch, J. P., Clasen, L. S., Greenstein, D., Berman, R., et al. (2014). Longitudinal four-dimensional mapping of subcortical anatomy in human development. Proceedings of the National Academy of Sciences of the United States of America, 111(4), 1592–1597.  https://doi.org/10.1073/pnas.1316911111.CrossRefGoogle Scholar
  47. Setänen, S., Lehtonen, L., Parkkola, R., Aho, K., & Haataja, L. the, P. S. G.(2016). Prediction of neuromotor outcome in infants born preterm at 11 years of age using volumetric neonatal magnetic resonance imaging and neurological examinations. Developmental Medicine and Child Neurology, 58(7), 721–727.  https://doi.org/10.1111/dmcn.13030.CrossRefGoogle Scholar
  48. Srinivasan, L., Dutta, R., Counsell, S. J., Allsop, J. M., Boardman, J. P., Rutherford, M. A., et al. (2007). Quantification of Deep Gray Matter in Preterm Infants at Term-Equivalent Age Using Manual Volumetry of 3-Tesla Magnetic Resonance Images. Pediatrics, 119(4), 759–765.  https://doi.org/10.1542/peds.2006-2508.CrossRefGoogle Scholar
  49. Taylor, H. G., Filipek, P. A., Juranek, J., Bangert, B., Minich, N., & Hack, M. (2011). Brain volumes in adolescents with very low birth weight: Effects on brain structure and associations with neuropsychological outcomes. Developmental Neuropsychology, 36(1), 96–117.  https://doi.org/10.1080/87565641.2011.540544.CrossRefGoogle Scholar
  50. Verney, C., Pogledic, I., Biran, V., Adle-Biassette, H., Fallet-Bianco, C., & Gressens, P. (2012). Microglial reaction in axonal crossroads is a hallmark of noncystic periventricular white matter injury in very preterm infants. Journal of Neuropathology and Experimental Neurology, 71(3), 251–264.  https://doi.org/10.1097/NEN.0b013e3182496429.CrossRefGoogle Scholar
  51. Wechsler, D. (1999). Wechsler abbreviated scale of intelligence (WASI). New York: The Psychological Corporation.Google Scholar
  52. 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(0), 67–72.  https://doi.org/10.1016/j.neuroimage.2014.03.072.CrossRefGoogle Scholar
  53. Wilkinson, G. S., & Robertson, G. J. (2006). Wide Range Achievement Test (WRAT4) (4th ed.). Lutz: Psychological Assessment Resources.Google Scholar
  54. Yoon, U., Fonov, V. S., Perusse, D., & Evans, A. C. (2009). The effect of template choice on morphometric analysis of pediatric brain data. Neuroimage, 45(3), 769–777.  https://doi.org/10.1016/j.neuroimage.2008.12.046.CrossRefGoogle Scholar
  55. Young, J. M., Powell, T. L., Morgan, B. R., Card, D., Lee, W., Smith, M. L., et al. (2015). Deep grey matter growth predicts neurodevelopmental outcomes in very preterm children. NeuroImage, 111, 360–368.  https://doi.org/10.1016/j.neuroimage.2015.02.030.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Wai Yen Loh
    • 1
    • 2
    • 3
    Email author
  • Peter J. Anderson
    • 1
    • 4
    • 5
  • Jeanie L. Y. Cheong
    • 1
    • 6
    • 7
  • Alicia J. Spittle
    • 1
    • 6
    • 8
  • Jian Chen
    • 1
    • 9
  • Katherine J. Lee
    • 1
    • 4
  • Charlotte Molesworth
    • 1
  • Terrie E. Inder
    • 10
  • Alan Connelly
    • 2
    • 3
  • Lex W. Doyle
    • 1
    • 6
    • 7
  • Deanne K. Thompson
    • 1
    • 2
    • 4
  1. 1.Murdoch Childrens Research InstituteMelbourneAustralia
  2. 2.Florey Institute of Neuroscience and Mental HealthMelbourneAustralia
  3. 3.The Florey Department of Neuroscience and Mental HealthUniversity of MelbourneMelbourneAustralia
  4. 4.Department of PediatricsUniversity of MelbourneMelbourneAustralia
  5. 5.Monash Institute of Cognitive and Clinical NeurosciencesMonash UniversityClaytonAustralia
  6. 6.Neonatal ServicesRoyal Women’s HospitalMelbourneAustralia
  7. 7.Department of Obstetrics and GynecologyUniversity of MelbourneMelbourneAustralia
  8. 8.Department of PhysiotherapyUniversity of MelbourneMelbourneAustralia
  9. 9.Department of Medicine, Stroke and Ageing Research Group, Southern Clinical SchoolMonash UniversityMelbourneAustralia
  10. 10.Brigham and Women’s HospitalBostonUSA

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