Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses.

ABSTRACT:

Background

The choice of preprocessing pipeline introduces variability in neuroimaging analyses that affects the reproducibility of scientific findings. Features derived from structural and functional MRI data are sensitive to the algorithmic or parametric differences of preprocessing tasks, such as image normalization, registration, and segmentation to name a few. Therefore it is critical to understand and potentially mitigate the cumulative biases of pipelines in order to distinguish biological effects from methodological variance.

Methods

Here we use an open structural MRI dataset (ABIDE), supplemented with the Human Connectome Project, to highlight the impact of pipeline selection on cortical thickness measures. Specifically, we investigate the effect of (i) software too

SUBMITTER: Bhagwat N 

PROVIDER: S-EPMC7821710 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature