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bioRxiv · 10.1101/2022.11.14.516460

Evidence for Embracing Normative Modeling

Rutherford, S.·Barkema, P.·Tso, I. F.·Sripada, C.·Beckmann, C. F.·Ruhe, H. G.·Marquand, A. F.
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Abstract

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 confirm the advantage (i.e., stronger effect sizes, more accurate classification and prediction) of using normative modeling features. We intend for these accessible resources to facilitate wider adoption of normative modeling across the neuroimaging community.

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Rutherford, S., Barkema, P., Tso, I. F., Sripada, C., Beckmann, C. F., Ruhe, H. G., Marquand, A. F.. 2022-11-15. Evidence for Embracing Normative Modeling. https://doi.org/10.1101/2022.11.14.516460

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