Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model

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.

Automated summary

Analyzing neuroimages is an important method in the fields of neuroscience and neurology, but incompatibilities across protocols and vendors pose a major problem. In this study, we successfully corrected center effects in cortical features obtained from multicenter MRIs. Our LME model-based w-score effectively corrected the center effects while preserving disease effects, as shown in various classification tasks. This model also harmonized data that were not used for model training, making it a valuable tool in neuroimaging analysis.