Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands; Donders Institute, Radboud University Nijmegen, Nijmegen, Netherlands; Department of Psychiatry, University of Michigan-Ann Arbor, Ann Arbor, United States
Pieter Barkema
Donders Institute, Radboud University Nijmegen, Nijmegen, Netherlands
Ivy F Tso
Department of Psychiatry, University of Michigan-Ann Arbor, Ann Arbor, United States; Department of Psychology, University of Michigan-Ann Arbor, Ann Arbor, United States
Department of Psychiatry, University of Michigan-Ann Arbor, Ann Arbor, United States; Department of Philosophy, University of Michigan-Ann Arbor, Ann Arbor, United States
Christian F Beckmann
Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands; Donders Institute, Radboud University Nijmegen, Nijmegen, Netherlands; Center for Functional MRI of the Brain (FMRIB), Nuffield Department for Clinical Neuroscience, Welcome Centre for Integrative Neuroimaging, Oxford University, Oxford, United Kingdom
Henricus G Ruhe
Donders Institute, Radboud University Nijmegen, Nijmegen, Netherlands; Department of Psychiatry, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
Andre F Marquand
Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands; Donders Institute, Radboud University Nijmegen, Nijmegen, Netherlands
In this work, we expand the normative model repository introduced in Rutherford et al., 2022a to include normative models charting lifespan trajectories of structural surface area and brain functional connectivity, measured using two unique resting-state network atlases (Yeo-17 and Smith-10), and an updated online platform for transferring these models to new data sources. We showcase the value of these models with a head-to-head comparison between the features output by normative modeling and raw data features in several benchmarking tasks: mass univariate group difference testing (schizophrenia versus control), classification (schizophrenia versus control), and regression (predicting general cognitive ability). Across all benchmarks, we show the advantage of using normative modeling features, with the strongest statistically significant results demonstrated in the group difference testing and classification tasks. We intend for these accessible resources to facilitate the wider adoption of normative modeling across the neuroimaging community.
