Sunday, Jun 23: 9:00 AM - 1:00 PM
Educational Course - Half Day (4 hours)
COEX
Room: Conference Room E 6
The advancement of normative models has significantly improved our ability to draw inferences at the individual level when analyzing neuroimaging data. However, it's crucial to note that all existing models have been developed based on cross-sectional data, primarily due to its prevalence and accessibility. While this choice is justifiable, given the current data landscape, the applicability of these models to infer longitudinal dynamics, a trend gaining popularity in the scientific community, raises questions.
This tutorial addresses the current gap by presenting a method tailored for pre-trained normative (cross-sectional) models, introducing what is termed a "z-diff score." This score, rooted in the Warped Bayesian Linear Regression normative model theory, functions as a type of z-score. Its role is to statistically characterize the observed change between two visits as either significant or non-significant [Fraza 2021, Rehák Bučková 2023].
Initially, I will provide a concise overview of the concept and application of pre-trained normative models. I will demonstrate how to download and execute them using sample data on Google Colab. Subsequently, I will explain the concept of the z-diff score, highlighting its underlying assumptions and data prerequisites. Finally, I will showcase the practical computation of the z-diff score and delineate its potential applications in subsequent analyses.
Stats:
(Duration: 30 min; Presenter: Barbora Rehák Bučková; Jupyter notebooks in follow along format; Programming language: Python; Libraries and Toolboxes: PCNToolkit and various standard python libraries; Degree of interactivity: 100%)
Relevant papers:
Bučková, Barbora Rehák, et al. Using Normative Models Pre-Trained on Cross-Sectional Data to Evaluate Longitudinal Changes in Neuroimaging Data. bioRxiv, 9 June 2023, p. 2023.06.09.544217. bioRxiv, https://doi.org/10.1101/2023.06.09.544217.
Fraza, Charlotte J., et al. ‘Warped Bayesian Linear Regression for Normative Modelling of Big Data’. NeuroImage, vol. 245, Dec. 2021, p. 118715. PubMed, https://doi.org/10.1016/j.neuroimage.2021.118715.