Simultaneous cluster structure learning and estimation of heterogeneous graphs for matrix-variate fMRI data
© 2022 The International Biometric Society..
Graphical models play an important role in neuroscience studies, particularly in brain connectivity analysis. Typically, observations/samples are from several heterogenous groups and the group membership of each observation/sample is unavailable, which poses a great challenge for graph structure learning. In this paper, we propose a method which can achieve Simultaneous Clustering and Estimation of Heterogeneous Graphs (briefly denoted as SCEHG) for matrix-variate functional magnetic resonance imaging (fMRI) data. Unlike the conventional clustering methods which rely on the mean differences of various groups, the proposed SCEHG method fully exploits the group differences of conditional dependence relationships among brain regions for learning cluster structure. In essence, by constructing individual-level between-region network measures, we formulate clustering as penalized regression with grouping and sparsity pursuit, which transforms the unsupervised learning into supervised learning. A modified difference of convex programming with the alternating direction method of multipliers (DC-ADMM) algorithm is proposed to solve the corresponding optimization problem. We also propose a generalized criterion to specify the number of clusters. Extensive simulation studies illustrate the superiority of the SCEHG method over some state-of-the-art methods in terms of both clustering and graph recovery accuracy. We also apply the SCEHG procedure to analyze fMRI data associated with attention-deficit hyperactivity disorder (ADHD), which illustrates its empirical usefulness.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:79 |
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| Enthalten in: |
Biometrics - 79(2023), 3 vom: 07. Sept., Seite 2246-2259 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liu, Dong [VerfasserIn] |
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| Links: |
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| Themen: |
Clustering |
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| Anmerkungen: |
Date Completed 13.09.2023 Date Revised 03.10.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1111/biom.13753 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM34536001X |
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| 245 | 1 | 0 | |a Simultaneous cluster structure learning and estimation of heterogeneous graphs for matrix-variate fMRI data |
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| 500 | |a Date Revised 03.10.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2022 The International Biometric Society. | ||
| 520 | |a Graphical models play an important role in neuroscience studies, particularly in brain connectivity analysis. Typically, observations/samples are from several heterogenous groups and the group membership of each observation/sample is unavailable, which poses a great challenge for graph structure learning. In this paper, we propose a method which can achieve Simultaneous Clustering and Estimation of Heterogeneous Graphs (briefly denoted as SCEHG) for matrix-variate functional magnetic resonance imaging (fMRI) data. Unlike the conventional clustering methods which rely on the mean differences of various groups, the proposed SCEHG method fully exploits the group differences of conditional dependence relationships among brain regions for learning cluster structure. In essence, by constructing individual-level between-region network measures, we formulate clustering as penalized regression with grouping and sparsity pursuit, which transforms the unsupervised learning into supervised learning. A modified difference of convex programming with the alternating direction method of multipliers (DC-ADMM) algorithm is proposed to solve the corresponding optimization problem. We also propose a generalized criterion to specify the number of clusters. Extensive simulation studies illustrate the superiority of the SCEHG method over some state-of-the-art methods in terms of both clustering and graph recovery accuracy. We also apply the SCEHG procedure to analyze fMRI data associated with attention-deficit hyperactivity disorder (ADHD), which illustrates its empirical usefulness | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a clustering | |
| 650 | 4 | |a graphical model | |
| 650 | 4 | |a matrix data | |
| 650 | 4 | |a network analysis | |
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| 700 | 1 | |a Zhao, Changwei |e verfasserin |4 aut | |
| 700 | 1 | |a He, Yong |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Lei |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Ying |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xinsheng |e verfasserin |4 aut | |
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