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Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data

Advances in imaging acquisition techniques allow multiple imaging modalities to be collected from the same subject. Each individual modality offers limited yet unique views of the functional, structural, or dynamic temporal features of the brain. Multimodal fusion provides effective ways to leverage...

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Autores principales: Qi, Shile, Silva, Rogers F., Zhang, Daoqiang, Plis, Sergey M., Miller, Robyn, Vergara, Victor M., Jiang, Rongtao, Zhi, Dongmei, Sui, Jing, Calhoun, Vince D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8837596/
https://www.ncbi.nlm.nih.gov/pubmed/34811846
http://dx.doi.org/10.1002/hbm.25720
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author Qi, Shile
Silva, Rogers F.
Zhang, Daoqiang
Plis, Sergey M.
Miller, Robyn
Vergara, Victor M.
Jiang, Rongtao
Zhi, Dongmei
Sui, Jing
Calhoun, Vince D.
author_facet Qi, Shile
Silva, Rogers F.
Zhang, Daoqiang
Plis, Sergey M.
Miller, Robyn
Vergara, Victor M.
Jiang, Rongtao
Zhi, Dongmei
Sui, Jing
Calhoun, Vince D.
author_sort Qi, Shile
collection PubMed
description Advances in imaging acquisition techniques allow multiple imaging modalities to be collected from the same subject. Each individual modality offers limited yet unique views of the functional, structural, or dynamic temporal features of the brain. Multimodal fusion provides effective ways to leverage these complementary perspectives from multiple modalities. However, the majority of current multimodal fusion approaches involving functional magnetic resonance imaging (fMRI) are limited to 3D feature summaries that do not incorporate its rich temporal information. Thus, we propose a novel three‐way parallel group independent component analysis (pGICA) fusion method that incorporates the first‐level 4D fMRI data (temporal information included) by parallelizing group ICA into parallel ICA via a unified optimization framework. A new variability matrix was defined to capture subject‐wise functional variability and then link it to the mixing matrices of the other two modalities. Simulation results show that the three‐way pGICA provides highly accurate cross‐modality linkage estimation under both weakly and strongly correlated conditions, as well as comparable source estimation under different noise levels. Results using real brain imaging data identified one linked functional–structural–diffusion component associated to differences between schizophrenia and controls. This was replicated in an independent cohort, and the identified components were also correlated with major cognitive domains. Functional network connectivity revealed visual–subcortical and default mode‐cerebellum pairs that discriminate between schizophrenia and controls. Overall, both simulation and real data results support the use of three‐way pGICA to identify multimodal spatiotemporal links and to pursue the study of brain disorders under a single unifying multimodal framework.
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spelling pubmed-88375962022-02-14 Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data Qi, Shile Silva, Rogers F. Zhang, Daoqiang Plis, Sergey M. Miller, Robyn Vergara, Victor M. Jiang, Rongtao Zhi, Dongmei Sui, Jing Calhoun, Vince D. Hum Brain Mapp Research Articles Advances in imaging acquisition techniques allow multiple imaging modalities to be collected from the same subject. Each individual modality offers limited yet unique views of the functional, structural, or dynamic temporal features of the brain. Multimodal fusion provides effective ways to leverage these complementary perspectives from multiple modalities. However, the majority of current multimodal fusion approaches involving functional magnetic resonance imaging (fMRI) are limited to 3D feature summaries that do not incorporate its rich temporal information. Thus, we propose a novel three‐way parallel group independent component analysis (pGICA) fusion method that incorporates the first‐level 4D fMRI data (temporal information included) by parallelizing group ICA into parallel ICA via a unified optimization framework. A new variability matrix was defined to capture subject‐wise functional variability and then link it to the mixing matrices of the other two modalities. Simulation results show that the three‐way pGICA provides highly accurate cross‐modality linkage estimation under both weakly and strongly correlated conditions, as well as comparable source estimation under different noise levels. Results using real brain imaging data identified one linked functional–structural–diffusion component associated to differences between schizophrenia and controls. This was replicated in an independent cohort, and the identified components were also correlated with major cognitive domains. Functional network connectivity revealed visual–subcortical and default mode‐cerebellum pairs that discriminate between schizophrenia and controls. Overall, both simulation and real data results support the use of three‐way pGICA to identify multimodal spatiotemporal links and to pursue the study of brain disorders under a single unifying multimodal framework. John Wiley & Sons, Inc. 2021-11-22 /pmc/articles/PMC8837596/ /pubmed/34811846 http://dx.doi.org/10.1002/hbm.25720 Text en © 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research Articles
Qi, Shile
Silva, Rogers F.
Zhang, Daoqiang
Plis, Sergey M.
Miller, Robyn
Vergara, Victor M.
Jiang, Rongtao
Zhi, Dongmei
Sui, Jing
Calhoun, Vince D.
Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data
title Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data
title_full Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data
title_fullStr Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data
title_full_unstemmed Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data
title_short Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data
title_sort three‐way parallel group independent component analysis: fusion of spatial and spatiotemporal magnetic resonance imaging data
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8837596/
https://www.ncbi.nlm.nih.gov/pubmed/34811846
http://dx.doi.org/10.1002/hbm.25720
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