Comparison of Brain Networks Based on Predictive Models of Connectivity

Deligianni, F. , Clayden, J. and Yang, G.-Z. (2019) Comparison of Brain Networks Based on Predictive Models of Connectivity. In: 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), Athens, Greece, 28-30 Oct 2019, pp. 115-121. ISBN 9781728146171 (doi:10.1109/BIBE.2019.00029)

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In this study we adopt predictive modelling to identify simultaneously commonalities and differences in multimodal brain networks acquired within subjects. Typically, predictive modelling of functional connectomes from structural connectomes explores commonalities across multimodal imaging data. However, direct application of multivariate approaches such as sparse Canonical Correlation Analysis (sCCA) applies on the vectorised elements of functional connectivity across subjects and it does not guarantee that the predicted models of functional connectivity are Symmetric Positive Matrices (SPD). We suggest an elegant solution based on the transportation of the connectivity matrices on a Riemannian manifold, which notably improves the prediction performance of the model. Randomised lasso is used to alleviate the dependency of the sCCA on the lasso parameters and control the false positive rate. Subsequently, the binomial distribution is exploited to set a threshold statistic that reflects whether a connection is selected or rejected by chance. Finally, we estimate the sCCA loadings based on a de-noising approach that improves the estimation of the coefficients. We validate our approach based on resting-state fMRI and diffusion weighted MRI data. Quantitative validation of the prediction performance shows superior performance, whereas qualitative results of the identification process are promising.

Item Type:Conference Proceedings
Additional Information:This work was supported by EPSRC under Grant (EP/R026092/1).
Glasgow Author(s) Enlighten ID:Deligianni, Dr Fani
Authors: Deligianni, F., Clayden, J., and Yang, G.-Z.
College/School:College of Science and Engineering > School of Computing Science

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