Abstract
Inferring effective brain connectivity network is a challenging task owing to perplexing noise effects, the curse of dimensionality, and inter-subject variability. However, most existing network inference methods are based on correlation analysis and consider the datum points individually, revealing limited information of the neuron interactions and ignoring the relations amongst the derivatives of the data. Hence, we proposed a novel ultra group-constrained sparse linear regression model for effective connectivity inference. This model utilizes not only the discrepancy between observed signals and the model prediction, but also the discrepancy between the associated weak derivatives of the observed and the model signals for a more accurate effective connectivity inference. What’s more, a group constraint is applied to minimize the inter-subject variability and the proposed modeling was validated on a mild cognitive impairment dataset with superior results achieved.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sporns, O.: Towards network substrates of brain disorders. Brain 137, 2117–2118 (2014). doi:10.1093/brain/awu148
Lee, H.L.D., Kang, H., Kim, B.N., Chung, M.K.: Sparse brain network recovery under compressed sensing. IEEE Trans. Med. Imaging 30(5), 1154–1165 (2011)
Wee, C.Y., Yap, P.T., Zhang, D., Wang, L., Shen, D.: Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification. Brain Struct. Funct. 219(2), 641–656 (2014). doi:10.1007/s00429-013-0524-8
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). doi:10.1109/TNNLS.2017.2709910
Li, Y., Wee, C.Y., Jie, B., Peng, Z.W., Shen, D.G.: Sparse multivariate autoregressive modeling for mild cognitive impairment classification. Neuroinformatics 12(3), 455–469 (2014). doi:10.1007/s12021-014-9221-x
Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3), 1059–1069 (2010). doi:10.1016/j.neuroimage.2009.10.003
Menze, B.H., Kelm, B.M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., Hamprecht, F.A.: A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinformatics 10 (2009)
Nir, T.M., Jahanshad, N., Villalon-Reina, J.E., Toga, A.W., Jack, C.R., Weiner, M.W., Thompson, P.M., Alzheimer’s Disease Neuroimaging Initiative (ADNI): Effectiveness of regional DTI measures in distinguishing Alzheimer’s disease, MCI, and normal aging. Neuroimage Clin 3, 180–195 (2013). doi:10.1016/j.nicl.2013.07.006
Salvatore, C., Cerasa, A., Battista, P., Gilardi, M.C., Quattrone, A., Castiglioni, I., Alzheimer’s Disease Neuroimaging Initiative: Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer’s disease: a machine learning approach. Front. Neurosci. 9, 307 (2015). doi:10.3389/Fnins.2015.00307
Jie, B., Zhang, D.Q., Gao, W., Wang, Q., Wee, C.Y., Shen, D.G.: Integration of network topological and connectivity properties for neuroimaging classification. IEEE Trans. Bio Med. Eng. 61(2), 576–589 (2014). doi:10.1109/Tbme.2013.2284195
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). Novel Effective Connectivity Network Inference 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_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-67389-9_37
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)