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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9247505/ https://www.ncbi.nlm.nih.gov/pubmed/35783130 http://dx.doi.org/10.3389/fnagi.2022.869387 |
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author | Kim, SeungWook Kim, Sung-Woo Noh, Young Lee, Phil Hyu Na, Duk L. Seo, Sang Won Seong, Joon-Kyung |
author_facet | Kim, SeungWook Kim, Sung-Woo Noh, Young Lee, Phil Hyu Na, Duk L. Seo, Sang Won Seong, Joon-Kyung |
author_sort | Kim, SeungWook |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9247505 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92475052022-07-02 Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model Kim, SeungWook Kim, Sung-Woo Noh, Young Lee, Phil Hyu Na, Duk L. Seo, Sang Won Seong, Joon-Kyung Front Aging Neurosci Neuroscience 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. Frontiers Media S.A. 2022-06-17 /pmc/articles/PMC9247505/ /pubmed/35783130 http://dx.doi.org/10.3389/fnagi.2022.869387 Text en Copyright © 2022 Kim, Kim, Noh, Lee, Na, Seo and Seong. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Kim, SeungWook Kim, Sung-Woo Noh, Young Lee, Phil Hyu Na, Duk L. Seo, Sang Won Seong, Joon-Kyung Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model |
title | Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model |
title_full | Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model |
title_fullStr | Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model |
title_full_unstemmed | Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model |
title_short | Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model |
title_sort | harmonization of multicenter cortical thickness data by linear mixed effect model |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9247505/ https://www.ncbi.nlm.nih.gov/pubmed/35783130 http://dx.doi.org/10.3389/fnagi.2022.869387 |
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