Maintainer: Andrew Chen, [email protected]
License: Artistic License 2.0
References: If you are using FCHarmony, please cite the following papers:
| Citation | |
|---|---|
| ComBat for functional connectivity | Yu, M., Linn, K. A., Cook, P. A., Phillips, M. L., McInnis, M., Fava, M., Trivedi, M. H., Weissman, M. M., Shinohara, R. T., & Sheline, Y. I. (2018). Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. Human Brain Mapping, 39(11), 4213–4227. https://doi.org/10.1002/hbm.24241 |
| Block-ComBat and FC-CovBat | Chen, A. A., Srinivasan, D., Pomponio, R., Fan, Y., Nasrallah, I. M., Resnick, S. M., Beason-Held, L. L., Davatzikos, C., Satterthwaite, T. D., Bassett, D. S., Shinohara, R. T., & Shou, H. (2021). Harmonizing Functional Connectivity Reduces Scanner Effects in Community Detection. https://doi.org/10.1101/2021.12.03.469269 |
The R package and its Github dependencies can be installed via devtools by running the following code
# install.packages("devtools")
devtools::install_github("andy1764/CovBat_Harmonization/R")
devtools::install_github("andy1764/FCHarmony")
Then, you can load this package via
library(FCHarmony)
The R package provides the fcComBat, blComBat, and fcCovBat functions for harmonization of functional connectivity. We include test_regress for regression-based site effect evaluation and plot_fc for plotting results from test_regress or plotting functional connectivity matrices.
Community detection on graphs constructed from functional magnetic resonance imaging (fMRI) data has led to important insights into brain functional organization. Large studies of brain community structure often include images acquired on multiple scanners across different studies. Differences in scanner can introduce variability into the downstream results, and these differences are often referred to as scanner effects. Such effects have been previously shown to significantly impact common network metrics.
In our preprint, we identify scanner effects in data-driven community detection results and related network metrics. We assess the performance of FC-ComBat and introduce two new methods for harmonizing function connectivity:
- Block-ComBat (Bl-ComBat), which leverages existing knowledge about network structure
- FC-CovBat, which harmonizes patterns of covariance in the data using CovBat.
We demonstrate that our new methods reduce scanner effects in community structure and network metrics. Our results highlight scanner effects in studies of brain functional organization and provide additional tools to address these unwanted effects. FCHarmony implements our harmonization methods and provides tools to evaluate harmonization performance.
The R and Python implementations of CovBat are available here and are extensions of ComBat implemented by Jean-Phillipe Fortin in R, Python, and Matlab in the ComBat package maintained by Jean-Philippe Fortin. Network analyses in our paper largely utilize the Brain Connectivity Toolbox version Version 2019-03-03.