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Pipeline comparisons of convolutional neural networks for structural connectomes: predicting sex across 3, 152 participants., , , , , and . EMBC, page 1692-1695. IEEE, (2020)General factors of white matter microstructure from DTI and NODDI in the developing brain., , , , , , , , , and 1 other author(s). NeuroImage, (2022)Strong intercorrelations among global graph-theoretic indices of structural connectivity in the human brain., , , , , , , and . NeuroImage, (2023)Hierarchical complexity of the adult human structural connectome., , , , , , and . NeuroImage, (2019)Widespread associations between trait conscientiousness and thickness of brain cortical regions., , , , , , , , and . NeuroImage, (2018)Polygenic risk score for schizophrenia and structural brain connectivity in older age: A longitudinal connectome and tractography study., , , , , , , , , and 3 other author(s). NeuroImage, (2018)Improving data availability for brain image biobanking in healthy subjects: Practice-based suggestions from an international multidisciplinary working group., , , , , , , , , and 10 other author(s). NeuroImage, (2017)Brain grey and white matter predictors of verbal ability traits in older age: The Lothian Birth Cohort 1936., , , , , , , and . NeuroImage, (2017)The effect of network thresholding and weighting on structural brain networks in the UK Biobank., , , , , , , and . NeuroImage, (2020)