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Mach Learn Med Imaging. 2017 Sep;10541:299-306. doi: 10.1007/978-3-319-67389-9_35. Epub 2017 Sep 7.

Structural Connectivity Guided Sparse Effective Connectivity for MCI Identification.

Author information

1
School of Automation Sciences and Electrical Engineering, Beihang University, Beijing, China.
2
School of Aeronautic Science and Engineering, Beihang University, Beijing, China.
3
Department of Radiology and BRIC, UNC Chapel Hill, Chapel Hill, NC, USA.
4
Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.

Abstract

Recent advances in network modelling techniques have enabled the study of neurological disorders at a whole-brain level based on functional connectivity inferred from resting-state magnetic resonance imaging (rs-fMRI) scan possible. However, constructing a directed effective connectivity, which provides a more comprehensive characterization of functional interactions among the brain regions, is still a challenging task particularly when the ultimate goal is to identify disease associated brain functional interaction anomalies. In this paper, we propose a novel method for inferring effective connectivity from multimodal neuroimaging data for brain disease classification. Specifically, we apply a newly devised weighted sparse regression model on rs-fMRI data to determine the network structure of effective connectivity with the guidance from diffusion tensor imaging (DTI) data. We further employ a regression algorithm to estimate the effective connectivity strengths based on the previously identified network structure. We finally utilize a bagging classifier to evaluate the performance of the proposed sparse effective connectivity network through identifying mild cognitive impairment from healthy aging.

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