PT  - JOURNAL ARTICLE
AU  - Gareth Ball
AU  - Chris Adamson
AU  - Richard Beare
AU  - Marc L. Seal
TI  - Modelling neuroanatomical variation due to age and sex during childhood and adolescence
AID  - 10.1101/126441
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 126441
4099  - http://biorxiv.org/content/early/2017/04/11/126441.short
4100  - http://biorxiv.org/content/early/2017/04/11/126441.full
AB  - Brain development is a dynamic process that follows a choreographed trajectory during childhood and adolescence with tissue-specific alterations that reflect complex and ongoing biological processes. Accurate identification and modelling of these anatomical processes in vivo with MRI may provide clinically useful imaging markers of individual variability in development. In this study, we build a model of age- and sex-related anatomical variation using multimodal imaging measures and manifold learning.Using publicly-available data from two large, independent developmental cohorts (n=768 and 862), we apply a multimodal machine learning approach combining measures of tissue volume, cortical area and cortical thickness into a low-dimensional data representation.We find that neuroanatomical variation due to age and sex can be captured by two orthogonal patterns of brain development and we use this model to predict age with a mean error of 1.6-2 years and sex with an accuracy of 80-84%.We present a framework for modelling anatomical development during childhood using low-dimensional data representations. This model accurately predicts age and sex based on image-derived markers of cerebral morphology and generalises well to independent populations.