A structural enriched functional network: An application to predict brain cognitive performance

•A new method incorporating GraphNet and simplex constraints is proposed to estimate interpretable and structural enriched functional brain networks.•An efficient optimization algorithm using the projected gradient descent method is proposed for the construction of structural enriched functional bra...

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Veröffentlicht in:	Medical image analysis Jg. 71; S. 102026
Hauptverfasser:	Kim, Mansu, Bao, Jingxuan, Liu, Kefei, Park, Bo-yong, Park, Hyunjin, Baik, Jae Young, Shen, Li
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier B.V 01.07.2021 Elsevier BV
Schlagworte:	Brain Cognitive ability Enrichment Functional anatomy Functional network Graph-constrained elastic net Magnetic resonance imaging Nervous system Neuroimaging Performance enhancement Performance prediction Regression models Simplex regression Structure-function coupling Structure-function relationships Structure-function coupling Graph-constrained elastic net Simplex regression Functional network
ISSN:	1361-8415, 1361-8423, 1361-8423
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Abstract	•A new method incorporating GraphNet and simplex constraints is proposed to estimate interpretable and structural enriched functional brain networks.•An efficient optimization algorithm using the projected gradient descent method is proposed for the construction of structural enriched functional brain networks.•Extensive experiments demonstrate the promise of the proposed structural enriched functional brain networks on predicting interesting behavioral outcomes. The structure-function coupling in brain networks has emerged as an important research topic in modern neuroscience. The structural network could provide the backbone of the functional network. The integration of the functional network with structural information can help us better understand functional communication in the brain. This paper proposed a method to accurately estimate the brain functional network enriched by the structural network from diffusion magnetic resonance imaging. First, we adopted a simplex regression model with graph-constrained Elastic Net to construct the functional networks enriched by the structural network. Then, we compared the constructed network characteristics of this approach with several state-of-the-art competing functional network models. Furthermore, we evaluated whether the structural enriched functional network model improves the performance for predicting the cognitive-behavioral outcomes. The experiments have been performed on 218 participants from the Human Connectome Project database. The results demonstrated that our network model improves network consistency and its predictive performance compared with several state-of-the-art competing functional network models. [Display omitted]
AbstractList	The structure-function coupling in brain networks has emerged as an important research topic in modern neuroscience. The structural network could provide the backbone of the functional network. The integration of the functional network with structural information can help us better understand functional communication in the brain. This paper proposed a method to accurately estimate the brain functional network enriched by the structural network from diffusion magnetic resonance imaging. First, we adopted a simplex regression model with graph-constrained Elastic Net to construct the functional networks enriched by the structural network. Then, we compared the constructed network characteristics of this approach with several state-of-the-art competing functional network models. Furthermore, we evaluated whether the structural enriched functional network model improves the performance for predicting the cognitive-behavioral outcomes. The experiments have been performed on 218 participants from the Human Connectome Project database. The results demonstrated that our network model improves network consistency and its predictive performance compared with several state-of-the-art competing functional network models. •A new method incorporating GraphNet and simplex constraints is proposed to estimate interpretable and structural enriched functional brain networks.•An efficient optimization algorithm using the projected gradient descent method is proposed for the construction of structural enriched functional brain networks.•Extensive experiments demonstrate the promise of the proposed structural enriched functional brain networks on predicting interesting behavioral outcomes. The structure-function coupling in brain networks has emerged as an important research topic in modern neuroscience. The structural network could provide the backbone of the functional network. The integration of the functional network with structural information can help us better understand functional communication in the brain. This paper proposed a method to accurately estimate the brain functional network enriched by the structural network from diffusion magnetic resonance imaging. First, we adopted a simplex regression model with graph-constrained Elastic Net to construct the functional networks enriched by the structural network. Then, we compared the constructed network characteristics of this approach with several state-of-the-art competing functional network models. Furthermore, we evaluated whether the structural enriched functional network model improves the performance for predicting the cognitive-behavioral outcomes. The experiments have been performed on 218 participants from the Human Connectome Project database. The results demonstrated that our network model improves network consistency and its predictive performance compared with several state-of-the-art competing functional network models. [Display omitted] The structure-function coupling in brain networks has emerged as an important research topic in modern neuroscience. The structural network could provide the backbone of the functional network. The integration of the functional network with structural information can help us better understand functional communication in the brain. This paper proposed a method to accurately estimate the brain functional network enriched by the structural network from diffusion magnetic resonance imaging. First, we adopted a simplex regression model with graph-constrained Elastic Net to construct the functional networks enriched by the structural network. Then, we compared the constructed network characteristics of this approach with several state-of-the-art competing functional network models. Furthermore, we evaluated whether the structural enriched functional network model improves the performance for predicting the cognitive-behavioral outcomes. The experiments have been performed on 218 participants from the Human Connectome Project database. The results demonstrated that our network model improves network consistency and its predictive performance compared with several state-of-the-art competing functional network models.The structure-function coupling in brain networks has emerged as an important research topic in modern neuroscience. The structural network could provide the backbone of the functional network. The integration of the functional network with structural information can help us better understand functional communication in the brain. This paper proposed a method to accurately estimate the brain functional network enriched by the structural network from diffusion magnetic resonance imaging. First, we adopted a simplex regression model with graph-constrained Elastic Net to construct the functional networks enriched by the structural network. Then, we compared the constructed network characteristics of this approach with several state-of-the-art competing functional network models. Furthermore, we evaluated whether the structural enriched functional network model improves the performance for predicting the cognitive-behavioral outcomes. The experiments have been performed on 218 participants from the Human Connectome Project database. The results demonstrated that our network model improves network consistency and its predictive performance compared with several state-of-the-art competing functional network models.
ArticleNumber	102026
Author	Liu, Kefei Kim, Mansu Park, Hyunjin Bao, Jingxuan Shen, Li Baik, Jae Young Park, Bo-yong
AuthorAffiliation	2 School of Arts and Sciences, University of Pennsylvania, PA, USA 1 Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA 4 School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, South Korea 5 Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea 3 McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
AuthorAffiliation_xml	– name: 5 Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea – name: 4 School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, South Korea – name: 1 Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA – name: 3 McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada – name: 2 School of Arts and Sciences, University of Pennsylvania, PA, USA
Author_xml	– sequence: 1 givenname: Mansu orcidid: 0000-0002-0785-4514 surname: Kim fullname: Kim, Mansu organization: Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA – sequence: 2 givenname: Jingxuan orcidid: 0000-0001-7127-3258 surname: Bao fullname: Bao, Jingxuan organization: School of Arts and Sciences, University of Pennsylvania, PA, USA – sequence: 3 givenname: Kefei surname: Liu fullname: Liu, Kefei organization: Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA – sequence: 4 givenname: Bo-yong orcidid: 0000-0001-7096-337X surname: Park fullname: Park, Bo-yong organization: McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada – sequence: 5 givenname: Hyunjin surname: Park fullname: Park, Hyunjin organization: School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, South Korea – sequence: 6 givenname: Jae Young orcidid: 0000-0001-8431-5277 surname: Baik fullname: Baik, Jae Young organization: School of Arts and Sciences, University of Pennsylvania, PA, USA – sequence: 7 givenname: Li orcidid: 0000-0002-5443-0503 surname: Shen fullname: Shen, Li email: Li.Shen@pennmedicine.upenn.edu organization: Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
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Keywords	Structure-function coupling Graph-constrained elastic net Simplex regression Functional network
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Credit Author Statement Author Contribution Statement Li Shen: Conceptualization, Supervision, Methodology, Writing - Review & Editing; Mansu Kim: Conceptualization, Writing - Original Draft, Methodology, Formal analysis, Investigation; Jingxuan Bao: Formal analysis; Kefei Liu: Methodology, Validation; Bo-yong Park: Investigation; Jae Young Baik: Visualization; Hyunjin Park: Writing - Review & Editing.
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Snippet	•A new method incorporating GraphNet and simplex constraints is proposed to estimate interpretable and structural enriched functional brain networks.•An... The structure-function coupling in brain networks has emerged as an important research topic in modern neuroscience. The structural network could provide the...
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SubjectTerms	Brain Cognitive ability Enrichment Functional anatomy Functional network Graph-constrained elastic net Magnetic resonance imaging Nervous system Neuroimaging Performance enhancement Performance prediction Regression models Simplex regression Structure-function coupling Structure-function relationships
Title	A structural enriched functional network: An application to predict brain cognitive performance
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