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Abstract #2577

Individualised prediction of infant and toddler brain growth using longitudinal normative models

Russell Macleod1,2, Chiara Casella1,2, Niall Bourke1, Muriel Bruchhage3,4, Aksel Leknes3,5, Ayo Zahra3, Daniel Scheiene3, James Cole6, Mariam Zabihi6, Francesca Biondo7, Kirsty Donald8,9, Viren D’Sa10, Sean Deoni10, and Jonathan O'Muircheartaigh1,2,11
1Department of Forensic & Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom, 2Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 3Institute of Social Sciences, University of Stavanger, Stavanger, Norway, 4Department of Radiology, Stavanger Medical Imaging Laboratory, Stavanger University Hospital, Stavanger, Norway, 5Developmental Imaging, Murdoch Children's Research institute, Royal Children's Hospital, Melbourne, Australia, 6University College London, London, United Kingdom, 7Hawkes Institute, Computer Science, University College London, London, United Kingdom, 8Neuroscience Institute, University of Cape Town, Cape Town, South Africa, 9Department of Paediatrics and Child Health, Red Cross War Memorial Children’s Hospital, University of Cape Town, Cape Town, South Africa, 10Warren Alpert Medical School, Brown University, Providence, RI, United States, 11MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom

Synopsis

Keywords: Neuro, Normal development, Longitudinal, Normative Modelling

Motivation: Normative modelling based growth charts see frequent use for assessment of childhood development, with a recent spate of work focusing on mapping MRI derived neuroanatomical changes over early life. We propose adding longitudinal information to traditionally cross-sectional models to enhance predictive ability and allow creation of individual developmental trajectories.

Goal(s): Investigate if longitudinal models of regional brain volume growth allow more accurate prediction of early life developmental trajectories.

Approach: Construct cross-sectional and longitudinal models for multiple brain regions in high-/low-field cohorts.
Assess cross-sectional and longitudinal model accuracy.

Results: Longitudinal models outperform cross-sectional models with lower error in all regions except the globus pallidus.

Impact: The addition of longitudinal information into regional volume growth models allows prediction of an individualised developmental trajectories. Comparisons can be made between an individual’s actual regional volume and predicted developmental and rather than position within a traditional population distribution.

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Abstract

Keywords