Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Xu, Huashuai, Hao, Yuxing, Zhang, Yunge, Zhou, Dongyue, Kärkkäinen, Tommi, Nickerson, Lisa D., Li, Huanjie, Cong, Fengyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
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
_version_ 1785084763907817472
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
work_keys_str_mv AT xuhuashuai harmonizationofmultisitefunctionalmridatawithdualprojectionbasedicamodel
AT haoyuxing harmonizationofmultisitefunctionalmridatawithdualprojectionbasedicamodel
AT zhangyunge harmonizationofmultisitefunctionalmridatawithdualprojectionbasedicamodel
AT zhoudongyue harmonizationofmultisitefunctionalmridatawithdualprojectionbasedicamodel
AT karkkainentommi harmonizationofmultisitefunctionalmridatawithdualprojectionbasedicamodel
AT nickersonlisad harmonizationofmultisitefunctionalmridatawithdualprojectionbasedicamodel
AT lihuanjie harmonizationofmultisitefunctionalmridatawithdualprojectionbasedicamodel
AT congfengyu harmonizationofmultisitefunctionalmridatawithdualprojectionbasedicamodel