A Decentralized ComBat Algorithm and Applications to Functional Network Connectivity

• Type of Material: Article
• Authors: Biozid Bostami, Georgia State University; Frank G Hillary, Pennsylvania State University; Harm Jan van der Horn, University of Groningen; Joukje van der Naalt, University of Groningen; Vince Calhoun, Emory University; Victor M Vergara, Georgia State University
• Language: English
• Date: 2022-03-15
• Publisher: FRONTIERS MEDIA SA
• Publication Version: Final Published Version
• Copyright Statement: © 2022 Bostami, Hillary, van der Horn, van der Naalt, Calhoun and Vergara.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3389/fneur.2022.826734
• Title of Journal or Parent Work: FRONTIERS IN NEUROLOGY
• Volume: 13
• Start Page: 826734
• End Page: 826734
• Grant/Funding Information: Multiple funds supported this project from the authors VC and FH. We want to thank NIH and NSF for their grants. The grants included in this project are NSF: 2112455 and NIH: R01DA040487 and R61NS120249.
• Keywords: HARMONIZATION; brain network; federated learning; Clinical Neurology; harmonization; neuroimage analysis; Neurosciences & Neurology; Science & Technology; PLATFORM; Neurosciences; Life Sciences & Biomedicine; functional connectivity; TRAUMATIC BRAIN-INJURY
• Research Categories: Computer Science; Psychology, General

Abstract

Recent studies showed that working with neuroimage data collected from different research facilities or locations may incur additional source dependency, affecting the overall statistical power. This problem can be mitigated with data harmonization approaches. Recently, the ComBat method has become commonly adopted for various neuroimage modalities. While open neuroimaging datasets are becoming more common, a substantial amount of data is still unable to be shared for various reasons. In addition, current approaches require moving all the data to a central location, which requires additional resources and creates redundant copies of the same datasets. To address these issues, we propose a decentralized harmonization approach that does not create redundant copies of the original datasets and performs remote operations on the datasets separately without sharing any individual subject data, ensuring a certain level of privacy and reducing regulatory hurdles. We proposed a novel approach called “Decentralized ComBat” which can harmonize datasets separately without combining the datasets. We tested our model by harmonizing functional network connectivity datasets from two traumatic brain injury studies in a decentralized way. Also, we used simulations to analyze the performance and scalability of our model when the number of data collection sites increases. We compare the output with centralized ComBat and show that the proposed approach produces similar results, increasing the sensitivity of the functional network connectivity analysis and validating our approach. Simulations show that our model can be easily scaled to many more datasets based on the requirement. In sum, we believe this provides a powerful tool, further complementing open data and allowing for integrating public and private datasets.