Investigating microstructural variation in the human hippocampus using non-negative matrix factorization

In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate...

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Published inNeuroImage (Orlando, Fla.) Vol. 207; p. 116348
Main Authors Patel, Raihaan, Steele, Christopher J., Chen, Anthony G.X., Patel, Sejal, Devenyi, Gabriel A., Germann, Jürgen, Tardif, Christine L., Chakravarty, M. Mallar
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 15.02.2020
Elsevier Limited
Elsevier
Subjects
Algorithms
Anisotropy
Brain - pathology
Cognition & reasoning
Decomposition
Diffusion Magnetic Resonance Imaging - methods
Female
Hippocampus
Hippocampus - pathology
Humans
Image Processing, Computer-Assisted - methods
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Medical imaging
Microstructure
MRI
Multimodal
Neuroimaging
Neuroimaging - methods
Non-negative matrix factorization
Population
Principal components analysis
Studies
White Matter - pathology
Multimodal
Non-negative matrix factorization
Microstructure
MRI
Hippocampus
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Abstract In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses. •We use OPNMF to identify 4 distinct microstructural components in the hippocampus.•Using multiple microstructure metrics improved spatial stability of components.•Variability of component level microstructure related to demographics and cognition.
AbstractList In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses.
In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses. •We use OPNMF to identify 4 distinct microstructural components in the hippocampus.•Using multiple microstructure metrics improved spatial stability of components.•Variability of component level microstructure related to demographics and cognition.
In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses.In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses.
ArticleNumber 116348
Author Chen, Anthony G.X.
Tardif, Christine L.
Germann, Jürgen
Steele, Christopher J.
Patel, Sejal
Chakravarty, M. Mallar
Devenyi, Gabriel A.
Patel, Raihaan
Author_xml – sequence: 1
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  surname: Patel
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– sequence: 2
  givenname: Christopher J.
  surname: Steele
  fullname: Steele, Christopher J.
  organization: Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Canada
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  givenname: Anthony G.X.
  surname: Chen
  fullname: Chen, Anthony G.X.
  organization: Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Canada
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  givenname: Sejal
  surname: Patel
  fullname: Patel, Sejal
  organization: Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Canada
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  givenname: Gabriel A.
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  surname: Tardif
  fullname: Tardif, Christine L.
  organization: Department of Biological and Biomedical Engineering, McGill University, Montreal, Canada
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  givenname: M. Mallar
  surname: Chakravarty
  fullname: Chakravarty, M. Mallar
  email: mallar@cobralab.ca
  organization: Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Canada
BackLink https://www.ncbi.nlm.nih.gov/pubmed/31715254$$D View this record in MEDLINE/PubMed
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ISSN 1053-8119
1095-9572
IngestDate Wed Aug 27 01:30:44 EDT 2025
Thu Jul 10 18:34:48 EDT 2025
Wed Aug 13 04:11:18 EDT 2025
Thu Apr 03 06:55:28 EDT 2025
Thu Apr 24 23:00:17 EDT 2025
Tue Jul 01 03:02:10 EDT 2025
Fri Feb 23 02:40:03 EST 2024
Tue Aug 26 18:33:00 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Keywords Multimodal
Non-negative matrix factorization
Microstructure
MRI
Hippocampus
Language English
License This is an open access article under the CC BY-NC-ND license.
Copyright © 2019 Elsevier Inc. All rights reserved.
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Snippet In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution...
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StartPage 116348
SubjectTerms Algorithms
Anisotropy
Brain - pathology
Cognition & reasoning
Decomposition
Diffusion Magnetic Resonance Imaging - methods
Female
Hippocampus
Hippocampus - pathology
Humans
Image Processing, Computer-Assisted - methods
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Medical imaging
Microstructure
MRI
Multimodal
Neuroimaging
Neuroimaging - methods
Non-negative matrix factorization
Population
Principal components analysis
Studies
White Matter - pathology
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Title Investigating microstructural variation in the human hippocampus using non-negative matrix factorization
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https://dx.doi.org/10.1016/j.neuroimage.2019.116348
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