Abstract
Based on the neuroimaging data from a large set of acquired brain injury patients, we investigate the feasibility of using machine learning for automatic prediction of individual consciousness level. Rather than using the traditional Pearson’s correlation-based brain functional network, which measures only the simple temporal synchronization of the BOLD signals from each pair of brain regions, we construct a high-order brain functional network that is capable of characterizing topographical information-based high-level functional associations among brain regions. In such a high-order brain network, each node represents the community of a brain region, described by a set of this region’s low-order functional associations with other brain regions, and each edge characterizes topographical similarity between a pair of such communities. Experimental results show that the high-order brain functional network enables a significant better classification for consciousness level and recovery outcome prediction.
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
Sharp, D.J., Scott, G., Leech, R.: Network dysfunction after traumatic brain injury. Nat. Rev. Neurol. 10, 156–166 (2014)
Shulman, R.G., Hyder, F., Rothman, D.L.: Baseline brain energy supports the state of consciousness. Proc. Natl. Acad. Sci. U.S.A. 106, 11096–11101 (2009)
Wu, X., Zou, Q., Hu, J., et al.: Intrinsic functional connectivity patterns predict consciousness level and recovery outcome in acquired brain injury. J. Neurosci. 35(37), 12932–12946 (2015)
Perri, C.D., Bahri, M.A., Amico, E.: Neural correlates of consciousness in patients who have emerged from a minimally conscious state: a cross-sectional multimodal imaging study. Lancet Neurol. 15, 830–842 (2016)
Qin, P., Wu, X., Huang, Z., et al.: How are different neural networks related to consciousness? Ann. Neurol. 78, 594–605 (2015)
Huang, S., Li, J., Sun, L., et al.: Learning brain connectivity of Alzheimer’s disease from neuroimaging data. In: Bengio, Y., et al. (eds.): NIPS 2009, pp. 808–816 (2009)
Qiao, L., Zhang, H., Kim, M., et al.: Estimating functional brain networks by incorporating a modularity prior. Neuroimage 141, 399–407 (2016)
Yu, R., Zhang, H., An, L., Chen, X., Wei, Z., Shen, D.: Correlation-weighted sparse group representation for brain network construction in mci classification. In: Ourselin, S., Joskowicz, L., Sabuncu, Mert R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 37–45. Springer, Cham (2016). doi:10.1007/978-3-319-46720-7_5
Schnakers, C.: Clinical assessment of patients with disorders of consciousness. Arch. Ital. Biol. 150, 36–43 (2012)
Teasdale, G., Jennett, B.: Assessment of coma and impaired consciousness. Pract. Scale Lancet 2, 81–84 (1974)
Giacino, J.T., Kalmar, K., Whyte, J.: The JFK coma recovery scale-revised: measurement characteristics and diagnostic utility. Arch. Phys. Med. Rehabil. 85, 2020–2029 (2004)
Jennett, B., Bond, M.: Assessment of outcome after severe brain damage. Lancet 1, 480–484 (1975)
Lee, H., Lee, D.S., Kang, H., et al.: Sparse brain network recovery under compressed sensing. IEEE Trans. Med. Imaging 30, 1154–1165 (2011)
Sporns, O.: Networks of the Brain. MIT Press, Cambridge (2011)
Lanckriet, G.R.G., Cristianini, N., Bartlett, P., et al.: Learning the kernel matrix with semidefinite programming. J. Mach. Learn. Res. 5, 27–72 (2004)
Munsell, B.C., Wee, C.Y., Keller, S.S., et al.: Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data. Neuroimage 118, 219–230 (2015)
Karegowda1, A.G., Manjunath, A.S., Jayaram, M.A.: Comparative study of attribute selection using gain ratio and correlation based feature selection. Int. J. Inf. Technol. Knowl. 22(2), 271–277 (2010)
Acknowledgements
This work is partially supported by National Natural Science Foundation of China (Grant Nos. 61403200), Natural Science Foundation of Jiangsu Province (Grant No. BK20140800), and NIH grants (EB006733, EB008374, EB009634, MH107815, AG041721, and AG042599).
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
Jia, X., Zhang, H., Adeli, E., Shen, D. (2017). Consciousness Level and Recovery Outcome Prediction Using High-Order Brain Functional Connectivity Network. In: Wu, G., Laurienti, P., Bonilha, L., Munsell, B. (eds) Connectomics in NeuroImaging. CNI 2017. Lecture Notes in Computer Science(), vol 10511. Springer, Cham. https://doi.org/10.1007/978-3-319-67159-8_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-67159-8_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67158-1
Online ISBN: 978-3-319-67159-8
eBook Packages: Computer ScienceComputer Science (R0)