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Men and women differ in specific cognitive abilities and in the expression ofseveral neuropsychiatric conditions. Such findings could be attributed to sexhormones, brain differences, as well as a number of environmental variables.Existing research on identifying sex-related differences in brain structurehave predominantly used cross-sectional studies to investigate, for instance,differences in average gray matter volumes (GMVs). In this article we explorethe potential of a recently proposed multi-view matrix factorisation (MVMF)methodology to study structural brain changes in men and women that occur fromadolescence to adulthood. MVMF is a multivariate variance decompositiontechnique that extends principal component analysis to "multi-view" datasets,i.e. where multiple and related groups of observations are available. In thisapplication, each view represents a different age group. MVMF identifies latentfactors explaining shared and age-specific contributions to the observedoverall variability in GMVs over time. These latent factors can be used toproduce low-dimensional visualisations of the data that emphasise age-specificeffects once the shared effects have been accounted for. The analysis of twodatasets consisting of individuals born prematurely as well as healthy controlsprovides evidence to suggest that the separation between males and femalesbecomes increasingly larger as the brain transitions from adolescence toadulthood. We report on specific brain regions associated to these varianceeffects.

Studying the brain from adolescence to adulthood through sparse multi-view matrix factorisations