@article {Ball126441, author = {Gareth Ball and Chris Adamson and Richard Beare and Marc L. Seal and the Pediatric Imaging, Neurocognition and Genetics}, title = {Modelling neuroanatomical variation due to age and sex during childhood and adolescence}, elocation-id = {126441}, year = {2017}, doi = {10.1101/126441}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Brain development is a dynamic process that follows a well-defined 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 multiple imaging metrics and manifold learning.Using publicly-available data from two large, independent developmental cohorts (n=768, 862), we apply a multi-metric 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 simultaneously predict age with a mean error of 1.5-1.6 years and sex with an accuracy of 81\%.We present a framework for modelling anatomical development during childhood using manifold embedding. This model accurately predicts age and sex based on image-derived markers of cerebral morphology and generalises well to independent populations.}, URL = {https://www.biorxiv.org/content/early/2017/07/16/126441}, eprint = {https://www.biorxiv.org/content/early/2017/07/16/126441.full.pdf}, journal = {bioRxiv} }