Day 1


Papers: Brain Development

Individualized assessment of regional brain volumes in neonates with Down syndrome reveals extreme deviation in white matter and cerebellum

Abi Fukami-Gartner1,2, Ana A. Baburamani1, Ralica Dimitrova1,5, Prachi A. Patkee1, Olatz Ojinaga Alfageme1,3, Alexandra F. Bonthrone1, Alena Uus1,4, Emer Hughes1, Maria Deprez1,4, Serena J. Counsell1, Joseph V. Hajnal1,4, A. David Edwards1,2, Jonathan O'Muircheartaigh1,2,5, and Mary A. Rutherford1,2

  1. Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, UK
  2. MRC Centre for Neurodevelopmental Disorders, King's College London, UK
  3. Centre for Brain and Cognitive Development, Birkbeck, University of London, UK
  4. Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, UK
  5. Department of Forensic and Neurodevelopmental Science, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK

Background: There are relatively few early neuro-imaging studies of Down syndrome (DS), despite being the most common genetic cause of intellectual disability, with characteristics present from birth.

Research question(s) and methods: The aim of this study was to identify brain regions, and tissue types, which deviate in volume from norm in neonates with DS using individual z-scores extracted from robust normative modelling. T2-weighted brain scans were acquired on a Philips Achieva 3T MRI scanner at St Thomas' Hospital (London) for neonates with DS (n = 20, 50% female) and typically developing controls (TDC, n = 493, 49% female) from the developing Human Connectome Project (dHCP). We generated normative curves of typical brain development from 32-46 weeks post-menstrual age using Gaussian Process Regression allowing z-scores for each brain region to be extracted for each individual neonate.

Results: The cingulate white matter (WM) (median z-score = -2.55, pFDR <0.0001), the cerebellum (median z-score = -1.73, pFDR <0.0001), the frontal WM (median z-score = -1.64, pFDR = 0.003), the insular WM (median z-score = -1.62, pFDR <0.0001), and the occipital WM (median z-score = -0.99, pFDR <0.0001) occupied significantly smaller proportions of the brain in DS neonates compared to TDCs. Furthermore, the lateral ventricles (median z-score = +1.07, pFDR <0.0001) were significantly larger than norm.

Conclusion: In addition to documented cerebellar hypoplasia, ventriculomegaly and reduced cortical GM volumes, here we have identified that neonates with DS have markedly reduced volumes in the cingulate, frontal, insular and occipital WM segments compared to TDC. In future, a correlative approach of individual neuro-developmental outcomes and regional brain deviations may help elucidate the aetiology of highly varied cognitive impairments and intellectual disability in DS.


Our aims are to:

  1. better understand the newborn brain in Down syndrome using specialist MRI.
  2. associate our early brain imaging findings with later developmental outcomes.
  3. understand the possible impact of congenital heart defects (CHDs) on early brain development.

Please do not hesitate to contact Professor Mary Rutherford ( (assisted by Mr Abi Gartner if you know of any Mothers who have recently received an antenatal or postnatal diagnosis of a child with Down syndrome, and may be interested in understanding their child’s early brain development.

Take home messages:

  • total brain size difference – don’t know how much this matters
  • Some regions are smaller than expected – soe of these areas may be associated with visual reception, receptive/expressive language and motor development
  • An associated CHD may affect early brain development – important to conduct further research to help support newborns with a CHD in early postnatal life.

Future aims:

  • Associate brain imaging with developmental outcomes
  • Investigate key brain regions further
  • Impact of CHDs
  • Better understand heterogeneity in outcomes