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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akhoondzadeh, M.: Decision tree, bagging and random forest methods detect TEC seismo-ionospheric anomalies around the time of the chile, (m\(-\)w = 8.8) earthquake of 27 february 2010. Adv. Space Res. 57(12), 856–867 (2016)
Allen, E.A., Damaraju, E., Plis, S.M., Erhardt, E.B., Eichele, T., Calhoun, V.D.: Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex 24, 663–676 (2012)
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex networks: structure and dynamics. Phys. Rep. 424(4), 175–308 (2006)
Friston, K.J.: Functional and effective connectivity: a review. Brain Connect. 1(1), 13–36 (2011)
Gauthier, S., Reisberg, B., Zaudig, M., Petersen, R.C., Ritchie, K., Broich, K., Belleville, S., Brodaty, H., Bennett, D., Chertkow, H.: Mild cognitive impairment. The Lancet 367(9518), 1262–1270 (2006)
Li, Y., Cui, W.G., Guo, Y.Z., Huang, T., Yang, X.F., Wei, H.L.: Time-varying system identification using an ultra-orthogonal forward regression and multiwavelet basis functions with applications to EEG. IEEE Trans. Neural Netw. Learn. Syst. PP(99), 1–13 (2017)
Liu, F., Wee, C.Y., Chen, H., Shen, D.: Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer’s disease and mild cognitive impairment identification. Neuroimage 84, 466–475 (2014)
Salvatore, C., Cerasa, A., Battista, P., Gilardi, M.C., Quattrone, A., Castiglioni, I.: Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer’s disease: a machine learning approach. Front. Neurosci. 9, 307 (2015)
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1), 273–289 (2002)
Wang, K., Liang, M., Wang, L., Tian, L., Zhang, X., Li, K., Jiang, T.: Altered functional connectivity in early Alzheimer’s disease: a restingstate fMRI study. Hum. Brain Mapp. 28(10), 967–978 (2007)
Wang, Z., Nie, B., Li, D., Zhao, Z., Han, Y., Song, H., Xu, J., Shan, B., Lu, J., Li, K.: Effect of acupuncture in mild cognitive impairment and Alzheimer disease: a functional MRI study. PLoS One 7(8), e42730 (2012)
Wee, C.Y., Yap, P.T., Zhang, D., Denny, K., Browndyke, J.N., Potter, G.G., Welsh-Bohmer, K.A., Wang, L., Shen, D.: Identification of MCI individuals using structural and functional connectivity networks. Neuroimage 59(3), 2045–2056 (2012)
Xu, L., Wu, X., Li, R., Chen, K., Long, Z., Zhang, J., Guo, X., Yao, L.: Prediction of progressive mild cognitive impairment by multi-modal neuroimaging biomarkers. J. Alzheimer’s Dis. 51(4), 1045–1056 (2016)
Zou, H.: The adaptive lasso and its oracle properties. JASA 101(476), 1418–1429 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Li, Y. et al. (2017). Structural Connectivity Guided Sparse Effective Connectivity for MCI Identification. In: Wang, Q., Shi, Y., Suk, HI., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2017. Lecture Notes in Computer Science(), vol 10541. Springer, Cham. https://doi.org/10.1007/978-3-319-67389-9_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-67389-9_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67388-2
Online ISBN: 978-3-319-67389-9
eBook Packages: Computer ScienceComputer Science (R0)