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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Zusammenfassung:•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]
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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.
ISSN:1361-8415
1361-8423
1361-8423
DOI:10.1016/j.media.2021.102026