Empirical Bayes estimation of pairwise maximum entropy model for nonlinear brain state dynamics
The pairwise maximum entropy model (pMEM) has recently gained widespread attention to exploring the nonlinear characteristics of brain state dynamics observed in resting-state functional magnetic resonance imaging (rsfMRI). Despite its unique advantageous features, the practical application of pMEM...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-12-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811921008910 |
| _version_ | 1819117319313424384 |
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| author | Seok-Oh Jeong Jiyoung Kang Chongwon Pae Jinseok Eo Sung Min Park Junho Son Hae-Jeong Park |
| author_facet | Seok-Oh Jeong Jiyoung Kang Chongwon Pae Jinseok Eo Sung Min Park Junho Son Hae-Jeong Park |
| author_sort | Seok-Oh Jeong |
| collection | DOAJ |
| description | The pairwise maximum entropy model (pMEM) has recently gained widespread attention to exploring the nonlinear characteristics of brain state dynamics observed in resting-state functional magnetic resonance imaging (rsfMRI). Despite its unique advantageous features, the practical application of pMEM for individuals is limited as it requires a much larger sample than conventional rsfMRI scans. Thus, this study proposes an empirical Bayes estimation of individual pMEM using the variational expectation-maximization algorithm (VEM-MEM). The performance of the VEM-MEM is evaluated for several simulation setups with various sample sizes and network sizes. Unlike conventional maximum likelihood estimation procedures, the VEM-MEM can reliably estimate the individual model parameters, even with small samples, by effectively incorporating the group information as the prior. As a test case, the individual rsfMRI of children with attention deficit hyperactivity disorder (ADHD) is analyzed compared to that of typically developed children using the default mode network, executive control network, and salient network, obtained from the Healthy Brain Network database. We found that the nonlinear dynamic properties uniquely established on the pMEM differ for each group. Furthermore, pMEM parameters are more sensitive to group differences and are better associated with the behavior scores of ADHD compared to the Pearson correlation-based functional connectivity. The simulation and experimental results suggest that the proposed method can reliably estimate the individual pMEM and characterize the dynamic properties of individuals by utilizing empirical information of the group brain state dynamics. |
| first_indexed | 2024-12-22T05:31:05Z |
| format | Article |
| id | doaj.art-e21b354c171e4741a1e058e9587874b3 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-22T05:31:05Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-e21b354c171e4741a1e058e9587874b32022-12-21T18:37:27ZengElsevierNeuroImage1095-95722021-12-01244118618Empirical Bayes estimation of pairwise maximum entropy model for nonlinear brain state dynamicsSeok-Oh Jeong0Jiyoung Kang1Chongwon Pae2Jinseok Eo3Sung Min Park4Junho Son5Hae-Jeong Park6Department of Statistics, Hankuk University of Foreign Studies, Yong-In, Republic of KoreaCenter for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of KoreaCenter for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of KoreaCenter for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Graduate School of Medical Science, Yonsei University College of Medicine, Brain Korea 21 Project, Seoul, Republic of KoreaCenter for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of KoreaCenter for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Graduate School of Medical Science, Yonsei University College of Medicine, Brain Korea 21 Project, Seoul, Republic of KoreaCenter for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Graduate School of Medical Science, Yonsei University College of Medicine, Brain Korea 21 Project, Seoul, Republic of Korea; Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea; Corresponding author at: Graduate School of Medical Science, Department of Nuclear Medicine, Yonsei University, College of Medicine, 50-1 Yonsei-ro, Sinchon-dong, Seodaemun-gu, Seoul 03722, Republic of Korea.The pairwise maximum entropy model (pMEM) has recently gained widespread attention to exploring the nonlinear characteristics of brain state dynamics observed in resting-state functional magnetic resonance imaging (rsfMRI). Despite its unique advantageous features, the practical application of pMEM for individuals is limited as it requires a much larger sample than conventional rsfMRI scans. Thus, this study proposes an empirical Bayes estimation of individual pMEM using the variational expectation-maximization algorithm (VEM-MEM). The performance of the VEM-MEM is evaluated for several simulation setups with various sample sizes and network sizes. Unlike conventional maximum likelihood estimation procedures, the VEM-MEM can reliably estimate the individual model parameters, even with small samples, by effectively incorporating the group information as the prior. As a test case, the individual rsfMRI of children with attention deficit hyperactivity disorder (ADHD) is analyzed compared to that of typically developed children using the default mode network, executive control network, and salient network, obtained from the Healthy Brain Network database. We found that the nonlinear dynamic properties uniquely established on the pMEM differ for each group. Furthermore, pMEM parameters are more sensitive to group differences and are better associated with the behavior scores of ADHD compared to the Pearson correlation-based functional connectivity. The simulation and experimental results suggest that the proposed method can reliably estimate the individual pMEM and characterize the dynamic properties of individuals by utilizing empirical information of the group brain state dynamics.http://www.sciencedirect.com/science/article/pii/S1053811921008910Maximum entropy modelResting stateBrain dynamicsEnergy landscapeVariational BayesVariational expectation-maximization |
| spellingShingle | Seok-Oh Jeong Jiyoung Kang Chongwon Pae Jinseok Eo Sung Min Park Junho Son Hae-Jeong Park Empirical Bayes estimation of pairwise maximum entropy model for nonlinear brain state dynamics NeuroImage Maximum entropy model Resting state Brain dynamics Energy landscape Variational Bayes Variational expectation-maximization |
| title | Empirical Bayes estimation of pairwise maximum entropy model for nonlinear brain state dynamics |
| title_full | Empirical Bayes estimation of pairwise maximum entropy model for nonlinear brain state dynamics |
| title_fullStr | Empirical Bayes estimation of pairwise maximum entropy model for nonlinear brain state dynamics |
| title_full_unstemmed | Empirical Bayes estimation of pairwise maximum entropy model for nonlinear brain state dynamics |
| title_short | Empirical Bayes estimation of pairwise maximum entropy model for nonlinear brain state dynamics |
| title_sort | empirical bayes estimation of pairwise maximum entropy model for nonlinear brain state dynamics |
| topic | Maximum entropy model Resting state Brain dynamics Energy landscape Variational Bayes Variational expectation-maximization |
| url | http://www.sciencedirect.com/science/article/pii/S1053811921008910 |
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