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A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis

Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not...

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Autores principales: Sun, Mingyu, Gabrielson, Ben, Akhonda, Mohammad Abu Baker Siddique, Yang, Hanlu, Laport, Francisco, Calhoun, Vince, Adali, Tülay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256022/
https://www.ncbi.nlm.nih.gov/pubmed/37300060
http://dx.doi.org/10.3390/s23115333
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author Sun, Mingyu
Gabrielson, Ben
Akhonda, Mohammad Abu Baker Siddique
Yang, Hanlu
Laport, Francisco
Calhoun, Vince
Adali, Tülay
author_facet Sun, Mingyu
Gabrielson, Ben
Akhonda, Mohammad Abu Baker Siddique
Yang, Hanlu
Laport, Francisco
Calhoun, Vince
Adali, Tülay
author_sort Sun, Mingyu
collection PubMed
description Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not be effective if the data’s true latent dimensionality is not adequately modeled, where severe overparameterization may lead to poor separation and time performance. In this paper, we propose a scalable JBSS method by modeling and separating the “shared” subspace from the data. The shared subspace is defined as the subset of latent sources that exists across all datasets, represented by groups of sources that collectively form a low-rank structure. Our method first provides the efficient initialization of the independent vector analysis (IVA) with a multivariate Gaussian source prior (IVA-G) specifically designed to estimate the shared sources. Estimated sources are then evaluated regarding whether they are shared, upon which further JBSS is applied separately to the shared and non-shared sources. This provides an effective means to reduce the dimensionality of the problem, improving analyses with larger numbers of datasets. We apply our method to resting-state fMRI datasets, demonstrating that our method can achieve an excellent estimation performance with significantly reduced computational costs.
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spelling pubmed-102560222023-06-10 A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis Sun, Mingyu Gabrielson, Ben Akhonda, Mohammad Abu Baker Siddique Yang, Hanlu Laport, Francisco Calhoun, Vince Adali, Tülay Sensors (Basel) Article Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not be effective if the data’s true latent dimensionality is not adequately modeled, where severe overparameterization may lead to poor separation and time performance. In this paper, we propose a scalable JBSS method by modeling and separating the “shared” subspace from the data. The shared subspace is defined as the subset of latent sources that exists across all datasets, represented by groups of sources that collectively form a low-rank structure. Our method first provides the efficient initialization of the independent vector analysis (IVA) with a multivariate Gaussian source prior (IVA-G) specifically designed to estimate the shared sources. Estimated sources are then evaluated regarding whether they are shared, upon which further JBSS is applied separately to the shared and non-shared sources. This provides an effective means to reduce the dimensionality of the problem, improving analyses with larger numbers of datasets. We apply our method to resting-state fMRI datasets, demonstrating that our method can achieve an excellent estimation performance with significantly reduced computational costs. MDPI 2023-06-05 /pmc/articles/PMC10256022/ /pubmed/37300060 http://dx.doi.org/10.3390/s23115333 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sun, Mingyu
Gabrielson, Ben
Akhonda, Mohammad Abu Baker Siddique
Yang, Hanlu
Laport, Francisco
Calhoun, Vince
Adali, Tülay
A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis
title A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis
title_full A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis
title_fullStr A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis
title_full_unstemmed A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis
title_short A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis
title_sort scalable approach to independent vector analysis by shared subspace separation for multi-subject fmri analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256022/
https://www.ncbi.nlm.nih.gov/pubmed/37300060
http://dx.doi.org/10.3390/s23115333
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