CBICA has developed a statistically-based harmonization technique for pooling large neuroimaging datasets across the lifespan. The method, called ComBat+GAM, was recently published in Neuroimage (https://doi.org/10.1016/j.neuroimage.2019.116450) and utilizes generalized additive models (GAMs) to estimate robust age trends in structural brain measurements, while simultaneously correcting for differences among datasets.
As the medical imaging research community has witnessed a rapid growth of interest in acquiring and analyzing large multi-center datasets, one obstacle remaining in multi-center collaboration is the issue of inter-scanner variability. Systematic differences between images acquired from different scanners are inevitable, and harmonization is the process of removing these systematic differences via statistical methods. ComBat+GAM is an extension of an algorithm originally developed for batch-effect correction in genomics (https://doi.org/10.1093/biostatistics/kxj037) while similar adaptations have been published for diffusion tensor imaging data (https://doi.org/10.1016/j.neuroimage.2017.08.047), cortical thickness measurements (https://doi.org/10.1016/j.neuroimage.2017.11.024), and functional connectivity matrices from resting-state fMRI scans (https://doi.org/10.1002/hbm.24241).
The continued development of image harmonization techniques is critical for the efficacy of future imaging studies. Such techniques enable multiple research centers to collaborate and derive greater power from their results than when working independently. Given the high economic costs of imaging, multi-center collaboration is the most feasible way to acquire large imaging datasets. Current harmonization methods show strong promise of enabling multi-center collaborations across the medical imaging community.