Learning site-invariant features of connectomes to harmonize complex network measures

Nancy R. Newlin; Praitayini Kanakaraj; Thomas Li; Kimberly Pechman; Derek Archer; Angela Jefferson; Bennett Landman; Daniel Moyer

doi:10.1117/12.3009645

2 April 2024 Learning site-invariant features of connectomes to harmonize complex network measures

Nancy R. Newlin, Praitayini Kanakaraj, Thomas Li, Kimberly Pechman, Derek Archer, Angela Jefferson, Bennett Landman, Daniel Moyer

Author Affiliations +

Proceedings Volume 12930, Medical Imaging 2024: Clinical and Biomedical Imaging; 129302E (2024) https://doi.org/10.1117/12.3009645
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Multi-site diffusion MRI data is often acquired on different scanners and with distinct protocols. Differences in hardware and acquisition result in data that contains site dependent information, which confounds connectome analyses aiming to combine such multi-site data. We propose a data-driven solution that isolates site-invariant information whilst maintaining relevant features of the connectome. We construct a latent space that is uncorrelated with the imaging site and highly correlated with patient age and a connectome summary measure. Here, we focus on network modularity. The proposed model is a conditional, variational autoencoder with three additional prediction tasks: one for patient age, and two for modularity trained exclusively on data from each site. This model enables us to 1) isolate site-invariant biological features, 2) learn site context, and 3) re-inject site context and project biological features to desired site domains. We tested these hypotheses by projecting 77 connectomes from two studies and protocols (Vanderbilt Memory and Aging Project (VMAP) and Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) to a common site. We find that the resulting dataset of modularity has statistically similar means (p-value ⪅0.05) across sites. In addition, we fit a linear model to the joint dataset and find that positive correlations between age and modularity were preserved.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nancy R. Newlin, Praitayini Kanakaraj, Thomas Li, Kimberly Pechman, Derek Archer, Angela Jefferson, Bennett Landman, and Daniel Moyer "Learning site-invariant features of connectomes to harmonize complex network measures", Proc. SPIE 12930, Medical Imaging 2024: Clinical and Biomedical Imaging, 129302E (2 April 2024); https://doi.org/10.1117/12.3009645

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