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Harmonization of multi-site functional MRI data with dual-projection based ICA model
Modern neuroimaging studies frequently merge magnetic resonance imaging (MRI) data from multiple sites. A larger and more diverse group of participants can increase the statistical power, enhance the reliability and reproducibility of neuroimaging research, and obtain findings more representative of...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401882/ https://www.ncbi.nlm.nih.gov/pubmed/37547146 http://dx.doi.org/10.3389/fnins.2023.1225606 |
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author | Xu, Huashuai Hao, Yuxing Zhang, Yunge Zhou, Dongyue Kärkkäinen, Tommi Nickerson, Lisa D. Li, Huanjie Cong, Fengyu |
author_facet | Xu, Huashuai Hao, Yuxing Zhang, Yunge Zhou, Dongyue Kärkkäinen, Tommi Nickerson, Lisa D. Li, Huanjie Cong, Fengyu |
author_sort | Xu, Huashuai |
collection | PubMed |
description | Modern neuroimaging studies frequently merge magnetic resonance imaging (MRI) data from multiple sites. A larger and more diverse group of participants can increase the statistical power, enhance the reliability and reproducibility of neuroimaging research, and obtain findings more representative of the general population. However, measurement biases caused by site differences in scanners represent a barrier when pooling data collected from different sites. The existence of site effects can mask biological effects and lead to spurious findings. We recently proposed a powerful denoising strategy that implements dual-projection (DP) theory based on ICA to remove site-related effects from pooled data, demonstrating the method for simulated and in vivo structural MRI data. This study investigates the use of our DP-based ICA denoising method for harmonizing functional MRI (fMRI) data collected from the Autism Brain Imaging Data Exchange II. After frequency-domain and regional homogeneity analyses, two modalities, including amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo), were used to validate our method. The results indicate that DP-based ICA denoising method removes unwanted site effects for both two fMRI modalities, with increases in the significance of the associations between non-imaging variables (age, sex, etc.) and fMRI measures. In conclusion, our DP method can be applied to fMRI data in multi-site studies, enabling more accurate and reliable neuroimaging research findings. |
format | Online Article Text |
id | pubmed-10401882 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104018822023-08-05 Harmonization of multi-site functional MRI data with dual-projection based ICA model Xu, Huashuai Hao, Yuxing Zhang, Yunge Zhou, Dongyue Kärkkäinen, Tommi Nickerson, Lisa D. Li, Huanjie Cong, Fengyu Front Neurosci Neuroscience Modern neuroimaging studies frequently merge magnetic resonance imaging (MRI) data from multiple sites. A larger and more diverse group of participants can increase the statistical power, enhance the reliability and reproducibility of neuroimaging research, and obtain findings more representative of the general population. However, measurement biases caused by site differences in scanners represent a barrier when pooling data collected from different sites. The existence of site effects can mask biological effects and lead to spurious findings. We recently proposed a powerful denoising strategy that implements dual-projection (DP) theory based on ICA to remove site-related effects from pooled data, demonstrating the method for simulated and in vivo structural MRI data. This study investigates the use of our DP-based ICA denoising method for harmonizing functional MRI (fMRI) data collected from the Autism Brain Imaging Data Exchange II. After frequency-domain and regional homogeneity analyses, two modalities, including amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo), were used to validate our method. The results indicate that DP-based ICA denoising method removes unwanted site effects for both two fMRI modalities, with increases in the significance of the associations between non-imaging variables (age, sex, etc.) and fMRI measures. In conclusion, our DP method can be applied to fMRI data in multi-site studies, enabling more accurate and reliable neuroimaging research findings. Frontiers Media S.A. 2023-07-20 /pmc/articles/PMC10401882/ /pubmed/37547146 http://dx.doi.org/10.3389/fnins.2023.1225606 Text en Copyright © 2023 Xu, Hao, Zhang, Zhou, Kärkkäinen, Nickerson, Li and Cong. 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 Xu, Huashuai Hao, Yuxing Zhang, Yunge Zhou, Dongyue Kärkkäinen, Tommi Nickerson, Lisa D. Li, Huanjie Cong, Fengyu Harmonization of multi-site functional MRI data with dual-projection based ICA model |
title | Harmonization of multi-site functional MRI data with dual-projection based ICA model |
title_full | Harmonization of multi-site functional MRI data with dual-projection based ICA model |
title_fullStr | Harmonization of multi-site functional MRI data with dual-projection based ICA model |
title_full_unstemmed | Harmonization of multi-site functional MRI data with dual-projection based ICA model |
title_short | Harmonization of multi-site functional MRI data with dual-projection based ICA model |
title_sort | harmonization of multi-site functional mri data with dual-projection based ica model |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401882/ https://www.ncbi.nlm.nih.gov/pubmed/37547146 http://dx.doi.org/10.3389/fnins.2023.1225606 |
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