Abstract #3002

ComBat harmonization for multi-site fixel-based analysis using traveling subject dataset

Rui Zou^1,2, Koji Kamagata², Yuya Saito², Christina Andica^2,3, Wataru Uchida², Kaito Takabayashi², Sen Guo², Seina Yoshida^2,4, Rinako Iseki^2,4, Takafumi Kitagawa^1,2, Shohei Fujita^2,5, Toshiaki Akashi², Akihiko Wada², Keigo Shimoji^1,2,3, and Shigeki Aoki^1,2,3

¹Department of Data Science, Juntendo University Graduate School of Medicine, Tokyo, Japan, ²Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan, ³Faculty of Health Data Science, Juntendo University, Chiba, Japan, ⁴Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan, ⁵Department of Radiology, The University of Tokyo, Tokyo, Japan

Synopsis

Keywords: Data Processing, Data Processing, Diffusion MRI, harmonization, fixel-based analysis, multisite

Motivation: Although multi-site DWI with large sample size has high statistical power and is sensitive to the subtle microstructural tissue changes, different models or protocols-induced measurement biases affect the reliability and reproducibility of the study. Therefore, harmonization is necessary to improve this issue.

Goal(s): The goal of our study is to evaluate the effectiveness of ComBat harmonization in mitigating measurement biases in FBA measures.

Approach: Our study utilized a traveling-subject DWI dataset, while various FBA measures were calculated and subsequently harmonized using the ComBat method.

Results: Our findings demonstrated that ComBat harmonization could effectively mitigate site, model, and protocol-induced measurement biases in FBA measures.

Impact: A significant contribution of this study is the seamless integration of ComBat into the fixel-based framework, which may enhance the reliability and reproducibility of multi-site research, offering a valuable tool for investigating microstructural tissue changes in the large-scale, multi-site studies.

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