A Decentralized ComBat Algorithm and Applications to Functional Network Connectivity

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.

DeepComBat: A Statistically Motivated, Hyperparameter-Robust, Deep Learning Approach to Harmonization of Neuroimaging Data

“…This model has also been extended to unique data settings, such as those where covariate effects are non-linear, longitudinal data is present, decentralized learning is required, multiple batch variables should be corrected for, or traveling subjects are available (Bayer et al, 2022;Bostami et al, 2022;Chen et al, 2022b;Horng et al, 2022;Maikusa et al, 2021;Pomponio et al, 2020). In applied studies, ComBat-family methods have been widely used and shown to improve inference and generalizability of results (Acquitter et al, 2022;Bartlett et al, 2018;Bourbonne et al, 2021;Crombé et al, 2020;Fortin et al, 2018;Marek et al, 2019;Yu et al, 2018).…”