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

Mansu Kim, Jingxuan Bao, Kefei Liu, Bo-yong Park, Hyunjin Park, Jae Young Baik, Li Shen

Abstract: Highlights • 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. Abstract 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.