5. Methodological advances in normative modelling

COEX 

Most normative modelling frameworks are limited by specific model assumptions such as gaussianity and homoscedasticity. Further, predictions for new individuals are often limited to sites that have been in the training set of the normative model. This lecture will introduce advanced methodological developments in normative modelling that are able to address these topics, using the PCN-toolkit and extensions in a Jupyter notebook format .
The latest development of normative modelling algorithms include the introduction of the SHASH distribution (Dinga et al.) as the basis of the normative model. Models from the flexible SHASH family allow for the reliable fit of skewed and non-gaussian distributions (Dinga et al., de Boer et al., 2023). In a jupyter notebook and follow-along format using python, attendees will be walked through the settings and deployment of this more complex type of normative model.
The impact of site and scanner effects on the neuroimaging signal have become a highly discussed topic in clinical neuroimaging. In the second half of the session, the focus will be on site information transfer and making predictions for individuals from “unseen sites” (Bayer, et al., 2023). This will be illustrated by walking participants through the site adaptation and prediction step in the jupyter notebook. Further, it will be illustrated how the knowledge on site transfer can be used to access the potential of large, pre-trained normative models (Rutherford et al., 2022).

Stats:
(Duration: 30min; Presenter: Johanna M. M. Bayer; Format: Jupyter notebooks in follow along format; Programming language: Python; Libraries and Toolboxes: PCNToolkit and various standard python libraries; Degree of interactivity: 100% )

Relevant papers:
de Boer, A. A. A., Kia, S. M., Rutherford, S., Zabihi, M., Fraza, C., Barkema, P., Westlye, L. T.,
Andreassen, O. A., Hinne, M., Beckmann, C. F., & Marquand, A. (2022). Non-Gaussian Normative Modelling With Hierarchical Bayesian Regression. In bioRxiv (p. 2022.10.05.510988). https://doi.org/10.1101/2022.10.05.510988

Dinga, Richard, et al. Normative Modeling of Neuroimaging Data Using Generalized Additive Models of Location Scale and Shape. bioRxiv, 14 June 2021, p. 2021.06.14.448106. bioRxiv, https://doi.org/10.1101/2021.06.14.448106.

Bayer, J. M. M., Dinga, R., Kia, S. M., Kottaram, A. R., Wolfers, T., Lv, J., Zalesky, A., Schmaal, L., & Marquand, A. (2022). Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models. NeuroImage, 264, 119699.

Rutherford, S., Fraza, C., Dinga, R., Kia, S. M., Wolfers, T., Zabihi, M., Berthet, P., Worker, A., Verdi, S., Andrews, D., Han, L. K. M., Bayer, J. M. M., Dazzan, P., McGuire, P., Mocking, R. T., Schene, A., Sripada, C., Tso, I. F., Duval, E. R., … Marquand, A. F. (2022). Charting brain growth and aging at high spatial precision. eLife, 11, e72904.