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Welcome! This toolkit is is designed for multi-purpose tasks in clinical neuroimaging, including normative modelling, trend surface modelling in addition to providing implementations of a number of fundamental machine learning algorithms. If you use these tools, please consider to cite the references below
Topics:
Fundamentals of normative modelling
Marquand et al 2016 Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies. Molecular Psychiatry 71, p. 552-561
Marquand et al 2019 Conceptualising mental disorders as deviations from normative functioning Molecular Psychiatry 24, p. 1415–1424
Hierarchical Bayesian regression
Kia et al 2020. The 23rd Medical Image Computing and Computer Assisted Intervention (MICCAI) conference.
Trend surface modelling and Bayesian linear regression
Huertas et al 2017 A Bayesian spatial model for neuroimaging data based on biologically informed basis functions. NeuroImage 161 p. 134-148