Modeling the impact of MRI acquisition bias on structural connectomes: Harmonizing structural connectomes.

ABSTRACT: One way to increase the statistical power and generalizability of neuroimaging studies is to collect data at multiple sites or merge multiple cohorts. However, this usually comes with site-related biases due to the heterogeneity of scanners and acquisition parameters, negatively impacting sensitivity. Brain structural connectomes are not an exception: Being derived from T1-weighted and diffusion-weighted magnetic resonance images, structural connectivity is impacted by differences in imaging protocol. Beyond minimizing acquisition parameter differences, removing bias with postprocessing is essential. In this work we create, from the exhaustive Human Connectome Project Young Adult dataset, a resampled dataset of different b-values and spatial resolutions, modeling a cohort scanned ac

SUBMITTER: Patel J 

PROVIDER: S-EPMC11340995 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature