Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model

SeungWook Kim; Sung-Woo Kim; Young Noh; Phil Hyu Lee; Duk L Na; Sang Won Seo; Joon-Kyung Seong

doi:10.3389/fnagi.2022.869387

Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model

Front Aging Neurosci. 2022 Jun 17:14:869387. doi: 10.3389/fnagi.2022.869387. eCollection 2022.

Authors

SeungWook Kim¹, Sung-Woo Kim¹, Young Noh², Phil Hyu Lee³, Duk L Na^{4

5}, Sang Won Seo^{4

5

6

7}, Joon-Kyung Seong^{8

9

10}

Affiliations

¹ Department of Bio-Convergence Engineering, Korea University, Seoul, South Korea.
² Department of Neurology, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea.
³ Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea.
⁴ Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
⁵ Neuroscience Center, Samsung Medical Center, Seoul, South Korea.
⁶ Samsung Alzheimer Research Center, Center for Clinical Epidemiology, Samsung Medical Center, Seoul, South Korea.
⁷ Department of Health Sciences and Technology, Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, South Korea.
⁸ School of Biomedical Engineering, Korea University, Seoul, South Korea.
⁹ Department of Artificial Intelligence, Korea University, Seoul, South Korea.
¹⁰ Interdisciplinary Program in Precision Public Health, Korea University, Seoul, South Korea.

Abstract

Objective: Analyzing neuroimages being useful method in the field of neuroscience and neurology and solving the incompatibilities across protocols and vendors have become a major problem. We referred to this incompatibility as "center effects," and in this study, we attempted to correct such center effects of cortical feature obtained from multicenter magnetic resonance images (MRIs).

Methods: For MRI of a total of 4,321 multicenter subjects, the harmonized w-score was calculated by correcting biological covariates such as age, sex, years of education, and intercranial volume (ICV) as fixed effects and center information as a random effect. Afterward, we performed classification tasks using principal component analysis (PCA) and linear discriminant analysis (LDA) to check whether the center effect was successfully corrected from the harmonized w-score.

Results: First, an experiment was conducted to predict the dataset origin of a random subject sampled from two different datasets, and it was confirmed that the prediction accuracy of linear mixed effect (LME) model-based w-score was significantly closer to the baseline than that of raw cortical thickness. As a second experiment, we classified the data of the normal and patient groups of each dataset, and LME model-based w-score, which is biological-feature-corrected values, showed higher classification accuracy than the raw cortical thickness data. Afterward, to verify the compatibility of the dataset used for LME model training and the dataset that is not, intraobject comparison and w-score RMSE calculation process were performed.

Conclusion: Through comparison between the LME model-based w-score and existing methods and several classification tasks, we showed that the LME model-based w-score sufficiently corrects the center effects while preserving the disease effects from the dataset. We also showed that the preserved disease effects have a match with well-known disease atrophy patterns such as Alzheimer's disease or Parkinson's disease. Finally, through intrasubject comparison, we found that the difference between centers decreases in the LME model-based w-score compared with the raw cortical thickness and thus showed that our model well-harmonizes the data that are not used for the model training.

Keywords: Alzheimer’s disease; Parkinson’s disease; cortical thickness; linear mixed effect model; magnetic resonance imaging; multicenter data harmonization.