Harmonization of cortical thickness measurements across scanners and sites

By Jean-Philippe Fortin, N. Cullen, Yvette I. Sheline, Warren D. Taylor, Irem Aselcioglu, P. Cook, P. Adams, Crystal M. Cooper, Maurizio Fava, P. McGrath and 5 more. bioRxiv, 2017

1,415 citations recorded by the source corpus.

With the proliferation of multi-site neuroimaging studies, there is a greater need for handling non-biological variance introduced by differences in MRI scanners and acquisition protocols. Such unwanted sources of variation, which we refer to as “scanner effects”, can hinder the detection of imaging features associated with clinical covariates of interest and cause spurious findings. In this paper, we investigate scanner effects in two large multi-site studies on cortical thickness measurements, across a total of 11 scanners. We propose a set of general tools for visualizing and identifying scanner effects that are generalizable to other modalities. We then propose to use ComBat, a technique adopted from the genomics literature and recently applied to diffusion tensor imaging data, to combine and harmonize cortical thickness values across scanners. We show that ComBat removes unwanted sources of scan variability while simultaneously increasing the power and reproducibility of subsequent statistical analyses. We also show that ComBat is useful for combining imaging data with the goal of studying life-span trajectories in the brain.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a48ee96ec29683f8d135985219d6b6c10f643033

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Russell A. Poldrack
2. Krzysztof J. Gorgolewski
3. Oluwasanmi Koyejo
4. Oscar Esteban
5. Maurizio Fava
6. Yvette I. Sheline
7. Melvin G. McInnis
8. Daniel Birman

In Radiology, Nuclear Medicine and Imaging

1. Oluwasanmi Koyejo
2. Oscar Esteban
3. Russell A. Poldrack
4. Yvette I. Sheline
5. Daniel Birman
6. Russell T. Shinohara
7. Krzysztof J. Gorgolewski
8. P. Cook

Citations in the corpus

• MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites (2016, background): “Many efforts have been devoted to the normalization across sites of the intensities of T1w MRI [17].”
• Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data (2018, methodology): “…et al., 2015; Rath et al., 2016), we speculated that measurements such as DTI fractional anisotropy (Fortin et al., 2017), MRI cortical thickness (Fortin et al., 2018), and fMRI functional connectivity (the present study) would differ among the four sites (CU, MGH, TX, and UM) due to systematic…”