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Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based Approaches

Analysis of time-evolving data is crucial to understand the functioning of dynamic systems such as the brain. For instance, analysis of functional magnetic resonance imaging (fMRI) data collected during a task may reveal spatial regions of interest, and how they evolve during the task. However, capt...

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Autores principales: Acar, Evrim, Roald, Marie, Hossain, Khondoker M., Calhoun, Vince D., Adali, Tülay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9081795/
https://www.ncbi.nlm.nih.gov/pubmed/35546891
http://dx.doi.org/10.3389/fnins.2022.861402
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author Acar, Evrim
Roald, Marie
Hossain, Khondoker M.
Calhoun, Vince D.
Adali, Tülay
author_facet Acar, Evrim
Roald, Marie
Hossain, Khondoker M.
Calhoun, Vince D.
Adali, Tülay
author_sort Acar, Evrim
collection PubMed
description Analysis of time-evolving data is crucial to understand the functioning of dynamic systems such as the brain. For instance, analysis of functional magnetic resonance imaging (fMRI) data collected during a task may reveal spatial regions of interest, and how they evolve during the task. However, capturing underlying spatial patterns as well as their change in time is challenging. The traditional approach in fMRI data analysis is to assume that underlying spatial regions of interest are static. In this article, using fractional amplitude of low-frequency fluctuations (fALFF) as an effective way to summarize the variability in fMRI data collected during a task, we arrange time-evolving fMRI data as a subjects by voxels by time windows tensor, and analyze the tensor using a tensor factorization-based approach called a PARAFAC2 model to reveal spatial dynamics. The PARAFAC2 model jointly analyzes data from multiple time windows revealing subject-mode patterns, evolving spatial regions (also referred to as networks) and temporal patterns. We compare the PARAFAC2 model with matrix factorization-based approaches relying on independent components, namely, joint independent component analysis (ICA) and independent vector analysis (IVA), commonly used in neuroimaging data analysis. We assess the performance of the methods in terms of capturing evolving networks through extensive numerical experiments demonstrating their modeling assumptions. In particular, we show that (i) PARAFAC2 provides a compact representation in all modes, i.e., subjects, time, and voxels, revealing temporal patterns as well as evolving spatial networks, (ii) joint ICA is as effective as PARAFAC2 in terms of revealing evolving networks but does not reveal temporal patterns, (iii) IVA's performance depends on sample size, data distribution and covariance structure of underlying networks. When these assumptions are satisfied, IVA is as accurate as the other methods, (iv) when subject-mode patterns differ from one time window to another, IVA is the most accurate. Furthermore, we analyze real fMRI data collected during a sensory motor task, and demonstrate that a component indicating statistically significant group difference between patients with schizophrenia and healthy controls is captured, which includes primary and secondary motor regions, cerebellum, and temporal lobe, revealing a meaningful spatial map and its temporal change.
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spelling pubmed-90817952022-05-10 Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based Approaches Acar, Evrim Roald, Marie Hossain, Khondoker M. Calhoun, Vince D. Adali, Tülay Front Neurosci Neuroscience Analysis of time-evolving data is crucial to understand the functioning of dynamic systems such as the brain. For instance, analysis of functional magnetic resonance imaging (fMRI) data collected during a task may reveal spatial regions of interest, and how they evolve during the task. However, capturing underlying spatial patterns as well as their change in time is challenging. The traditional approach in fMRI data analysis is to assume that underlying spatial regions of interest are static. In this article, using fractional amplitude of low-frequency fluctuations (fALFF) as an effective way to summarize the variability in fMRI data collected during a task, we arrange time-evolving fMRI data as a subjects by voxels by time windows tensor, and analyze the tensor using a tensor factorization-based approach called a PARAFAC2 model to reveal spatial dynamics. The PARAFAC2 model jointly analyzes data from multiple time windows revealing subject-mode patterns, evolving spatial regions (also referred to as networks) and temporal patterns. We compare the PARAFAC2 model with matrix factorization-based approaches relying on independent components, namely, joint independent component analysis (ICA) and independent vector analysis (IVA), commonly used in neuroimaging data analysis. We assess the performance of the methods in terms of capturing evolving networks through extensive numerical experiments demonstrating their modeling assumptions. In particular, we show that (i) PARAFAC2 provides a compact representation in all modes, i.e., subjects, time, and voxels, revealing temporal patterns as well as evolving spatial networks, (ii) joint ICA is as effective as PARAFAC2 in terms of revealing evolving networks but does not reveal temporal patterns, (iii) IVA's performance depends on sample size, data distribution and covariance structure of underlying networks. When these assumptions are satisfied, IVA is as accurate as the other methods, (iv) when subject-mode patterns differ from one time window to another, IVA is the most accurate. Furthermore, we analyze real fMRI data collected during a sensory motor task, and demonstrate that a component indicating statistically significant group difference between patients with schizophrenia and healthy controls is captured, which includes primary and secondary motor regions, cerebellum, and temporal lobe, revealing a meaningful spatial map and its temporal change. Frontiers Media S.A. 2022-04-25 /pmc/articles/PMC9081795/ /pubmed/35546891 http://dx.doi.org/10.3389/fnins.2022.861402 Text en Copyright © 2022 Acar, Roald, Hossain, Calhoun and Adali. 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
Acar, Evrim
Roald, Marie
Hossain, Khondoker M.
Calhoun, Vince D.
Adali, Tülay
Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based Approaches
title Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based Approaches
title_full Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based Approaches
title_fullStr Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based Approaches
title_full_unstemmed Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based Approaches
title_short Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based Approaches
title_sort tracing evolving networks using tensor factorizations vs. ica-based approaches
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9081795/
https://www.ncbi.nlm.nih.gov/pubmed/35546891
http://dx.doi.org/10.3389/fnins.2022.861402
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