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:

Nutritive sucking abnormalities and brain microstructural abnormalities in infants with established brain injury: a pilot study

Journal of Perinatology volume 39, pages 1498–1508 (2019)Cite this article

Subjects

Abstract

Objective

To determine the relationship between nutritive sucking and microstructural integrity of sensorimotor tracts in newborns with brain injury.

Study design

Diffusion imaging was performed in ten newborns with brain injury. Nutritive sucking was assessed using Nfant®. The motor, sensory, and corpus callosum tracts were reconstructed via tractography. Fractional anisotropy, radial, axial, and mean diffusivity were estimated for these tracts. Multiple regression models were developed to test the association between sucking features and diffusion parameters.

Results

Low-sucking smoothness correlated with low-fractional anisotropy of motor tracts (p = 0.0096). High-sucking irregularity correlated with high-mean diffusivity of motor (p = 0.030) and corpus callosum tracts (p = 0.032). For sensory tracts, high-sucking irregularity (p = 0.018) and low-smoothness variability (p = 0.002) correlated with high-mean diffusivity.

Interpretation

We show a correlation between neuroimaging-demonstrated microstructural brain abnormalities and variations in sucking patterns of newborns. The consistency of this relationship should be shown on larger cohorts.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Code availability

The MATLAB code for the nutritive sucking analyses is available upon request.

References

  1. American Academy of Pediatrics. Hospital discharge of the high-risk neonate. Pediatrics. 2008;122:1119–26.

    Google Scholar 

  2. Browne JV, Ross ES. Eating as a neurodevelopmental process for high-risk newborns. Clin Perinatol. 2011;38:731–43.

    PubMed  Google Scholar 

  3. Muscatelli F, Bouret SG. Wired for eating: how is an active feeding circuitry established in the postnatal brain? Curr Opin Neurobiol. 2018;52:165–71.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. LaMantia AS, Moody SA, Maynard TM, Karpinski BA, Zohn IE, Mendelowitz D, et al. Hard to swallow: developmental biological insights into pediatric dysphagia. Dev Biol. 2016;409:329–42.

    CAS  PubMed  Google Scholar 

  5. Poore MA, Barlow SM. Suck predicts neuromotor integrity and developmental outcomes. Perspect Speech Sci Orofac Disord. 2009;19:44–51.

    Google Scholar 

  6. Barkat-Masih M, Saha C, Hamby DK, Ofner S, Golomb MR. Feeding problems in children with neonatal arterial ischemic stroke. J Child Neurol. 2010;25:867–72.

    PubMed  Google Scholar 

  7. Quattrocchi CC, Longo D, Delfino LN, Cilio MR, Piersigilli F, Capua MD, et al. Dorsal brain stem syndrome: MR imaging location of brain stem tegmental lesions in neonates with oral motor dysfunction. Am J Neuroradiol. 2010;31:1438–42.

    CAS  PubMed  Google Scholar 

  8. Mizuno K, Ueda A. Neonatal feeding performance as a predictor of neurodevelopmental outcome at 18 months. Dev Med Child Neurol. 2005;47:299–304.

    PubMed  Google Scholar 

  9. Reilly S, Skuse D, Poblete X. Prevalence of feeding problems and oral motor dysfunction in children with cerebral palsy: a community survey. J Pediatr. 1996;129:877–82.

    CAS  PubMed  Google Scholar 

  10. Slattery J, Morgan A, Douglas J. Early sucking and swallowing problems as predictors of neurodevelopmental outcome in children with neonatal brain injury: a systematic review. Dev Med Child Neurol. 2012;54:796–806.

    PubMed  Google Scholar 

  11. Bickell M, Barton C, Dow K, Fucile S. A systematic review of clinical and psychometric properties of infant oral motor feeding assessments. Devl Neurorehabil. 2018;21:351–61.

    Google Scholar 

  12. Tsai SW, Chen CH, Lin MC. Prediction for developmental delay on Neonatal Oral Motor Assessment Scale in preterm infants without brain lesion. Pedia Int. 2010;52:65–8. [PubMed: 19400913]

    Google Scholar 

  13. Wolthuis-Stigter MI, Luinge MR, da Costa SP, Krijnen WP, van der Schans CP, Bos AF. The association between sucking behavior in preterm infants and neurodevelopmental outcomes at 2 years of age. J Pedia. 2015;166:26–30.

    Google Scholar 

  14. Zhang X, Zhou M, Yin H, Dai Y, Li Y. The predictive value of early oral motor assessments for neurodevelopmental outcomes of moderately and late preterm infants. Medicine. 2017;96:e9207.

    PubMed  PubMed Central  Google Scholar 

  15. Medoff-Cooper B, Shults J, Kaplan J. Sucking behavior of preterm neonates as a predictor of developmental outcomes. J Dev Behav Pedia. 2009;30:16–22. [PubMed: 19194323]

    Google Scholar 

  16. Tamilia E, Taffoni F, Formica D, Ricci L, Schena E, Keller F, et al. Technological solutions and main indices for the assessment of newborns’ nutritive sucking: a review. Sensors. 2014;14:634–58.

    PubMed  Google Scholar 

  17. Tamilia E, Formica D, Scaini A, Taffoni F. An automated system for the analysis of newborns’ oral-motor behavior. IEEE Trans Neural Syst Rehabil Eng. 2016;24:1294–303.

    PubMed  Google Scholar 

  18. Capilouto GJ, Cunningham TJ, Mullineaux DR, Tamilia E, Papadelis C, Giannone PJ. Quantifying neonatal sucking performance: promise of new methods. Semin Speech Lang. 2017;38:147–58.

    PubMed  PubMed Central  Google Scholar 

  19. Tamilia E, Formica D, Visco AM, Scaini A, Taffoni F. An automated system for quantitative analysis of newborns’ oral-motor behavior and coordination during bottle feeding. In: 2015 37th Conf Proc IEEE Eng Med Biol Soc (EMBC), 25 Aug 2015. IEEE Milano, Italy; 2015 p. 7386–9.

  20. Inder TE, Anderson NJ, Spencer C, Wells S, Volpe JJ. White matter injury in the premature infant: a comparison between serial cranial sonographic and MR findings at term. Am J Neuroradiol. 2003;24:805–9.

    PubMed  Google Scholar 

  21. Wimberger DM, Roberts TP, Barkovich AJ, Prayer LM, Moseley ME, Kucharczyk J. Identification of” premyelination” by diffusion-weighted MRI. J Comput Assist Tomogr. 1995;19:28–33.

    CAS  PubMed  Google Scholar 

  22. Berman JI, Mukherjee P, Partridge SC, Miller SP, Ferriero DM, Barkovich AJ, et al. Quantitative diffusion tensor MRI fiber tractography of sensorimotor white matter development in premature infants. Neuroimage. 2005;27:862–71.

    PubMed  Google Scholar 

  23. Capilouto GJ, Cunningham TJ, Giannone PJ, Grider D. A comparison of the nutritive sucking performance of full term and preterm neonates at hospital discharge: a prospective study. Early Hum Dev. 2019;134:26–30.

    PubMed  Google Scholar 

  24. Soares J, Marques P, Alves V, Sousa N. A hitchhiker’s guide to diffusion tensor imaging. Front Neurosci. 2013;7:31.

    PubMed  PubMed Central  Google Scholar 

  25. Amaizu N, Shulman RJ, Schanler RJ, Lau C. Maturation of oral feeding skills in preterm infants. Acta Paediatr. 2008;97:61–7.

    CAS  PubMed  Google Scholar 

  26. Woolrich MW, Jbabdi S, Patenaude B, Chappell M, Makni S, Behrens T, et al. Bayesian analysis of neuroimaging data in FSL. NeuroImage. 2009;45:S173–86.

    PubMed  Google Scholar 

  27. Behrens TE, Berg HJ, Jbabdi S, Rushworth MF, Woolrich MW. Probablistic diffusion tractography with multiple fibre orientations: what can we gain? NeuroImage. 2007;34:144–55.

    CAS  PubMed  Google Scholar 

  28. Papadelis C, Butler EE, Rubenstein M, Sun L, Zollei L, Nimec D, et al. Reorganization of the somatosensory cortex in hemiplegic cerebral palsy associated with impaired sensory tracts. NeuroImage: Clin. 2018;17:198–212.

    Google Scholar 

  29. Oishi K, Mori S, Donohue PK, Ernst T, Anderson L, Buchthal S, et al. Multi-contrast human neonatal brain atlas: application to normal neonate development analysis. Neuroimage. 2011;56:8–20.

    PubMed  PubMed Central  Google Scholar 

  30. Guye M, Parker GJ, Symms M, Boulby P, Wheeler-Kingshott CA, Salek-Haddadi A, et al. Combined functional MRI and tractography to demonstrate the connectivity of the human primary motor cortex in vivo. Neuroimage. 2003;19:1349–60.

    PubMed  Google Scholar 

  31. Fiori S, Guzzetta A. Plasticity following early-life brain injury: insights from quantitative MRI. Semin Perinatol. 2015;39:141–6.

    PubMed  Google Scholar 

  32. Balasubramanian S, Melendez-Calderon A, Roby-Brami A, Burdet E. On the analysis of movement smoothness. J Neuroeng Rehabil. 2015;12:112.

    PubMed  PubMed Central  Google Scholar 

  33. Burdet E, Milner TE. Quantization of human motions and learning of accurate movements. Biol Cybern. 1998;78:307–18.

    CAS  PubMed  Google Scholar 

  34. Burdet E, Franklin DW, Milner TE. Human robotics: neuromechanics and motor control. MIT press, Cambridge, MA, USA; 2013.

    Google Scholar 

  35. Tamilia E, Delafield J, Fiore S, Taffoni F. An automatized system for the assessment of nutritive sucking behavior in infants: a preliminary analysis on term neonates. In: 2014 36th Conf Proc IEEE Eng Med Biol Soc (EMBC), 26 Aug 2014. IEEE, Chicago, Illinois, USA; 2014. p. 5752–55.

  36. Pierpaoli C, Basser PJ. Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med. 1996;36:893–906.

    CAS  PubMed  Google Scholar 

  37. Seki K, Fetz EE. Gating of sensory input at spinal and cortical levels during preparation and execution of voluntary movement. J Neurosci. 2012;32:890–902.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Hadders-algra M. Early human motor development: from variation to the ability to vary and adapt. Neurosci Biobehav Rev. 2018;90:411–27.

    PubMed  Google Scholar 

  39. Eishima K. The analysis of sucking behaviour in newborn infants. Early Hum Dev. 1991;27:163–73.

    CAS  PubMed  Google Scholar 

  40. Fabri M, Polonara G. Functional topography of human corpus callosum: an FMRI mapping study. Neural Plast. 2013;2013:251308.

    PubMed  PubMed Central  Google Scholar 

  41. Fabri M, Polonara G, Quattrini A, Salvolini U, Del Pesce M, Manzoni T. Role of the corpus callosum in the somatosensory activation of the ipsilateral cerebral cortex: an fMRI study of callosotomized patients. Eur J Neurosci. 1999;11:3983–94.

    CAS  PubMed  Google Scholar 

  42. Hynd GW, Semrud-Clikeman M, Lorys AR, Novey ES, Eliopulos D, Lyytinen H. Corpus callosum morphology in attention deficit-hyperactivity disorder: morphometric analysis of MRI. J Learn Disabil. 1991;24:141–6.

    CAS  PubMed  Google Scholar 

  43. Bancroft TW, Ian CP. Inference based on conditional specification. Int Stat Rev. 1977;45:117–28.

    Google Scholar 

Download references

Acknowledgements

We thank the participants and their families for taking part in this research. We would like to thank Helen Christou and Stella Kourembanas for their support and guidance throughout the project.

Author information

Authors and Affiliations

  1. Laboratory of Children’s Brain Dynamics, Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

    Eleonora Tamilia, Maria Rocchi & Christos Papadelis

  2. Fetal Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

    Eleonora Tamilia, Maria Rocchi, P Ellen Grant & Christos Papadelis

  3. Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

    Marianna S. Parker & Anne Hansen

  4. Laboratory of Biomedical Robotics and Biomicrosystems, Università Campus Bio-Medico di Roma, Rome, Italy

    Fabrizio Taffoni

  5. Jane and John Justin Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA

    Christos Papadelis

Authors
  1. Eleonora Tamilia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marianna S. Parker
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maria Rocchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fabrizio Taffoni
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anne Hansen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. P Ellen Grant
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Christos Papadelis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christos Papadelis.

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.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tamilia, E., Parker, M.S., Rocchi, M. et al. Nutritive sucking abnormalities and brain microstructural abnormalities in infants with established brain injury: a pilot study. J Perinatol 39, 1498–1508 (2019). https://doi.org/10.1038/s41372-019-0479-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41372-019-0479-6

This article is cited by

Search

Advanced search

Quick links