Abstract
Witnessed by recent studies, functional connectivity is a useful tool in extracting brain network features and finding biomarkers for brain disease diagnosis. It still remains, however, challenging for the estimation of a functional connectivity from fMRI due to the high dimensional nature. In order to tackle this problem, we utilize a group sparse representation along with a structural equation model. Unlike the conventional group sparse representation, we devise a novel supervised discriminative group sparse representation by penalizing a large within-class variance and a small between-class variance of features. Thanks to the devised penalization term, we can learn connectivity coefficients that are similar within the same class and distinct between classes, thus helping enhance the diagnostic accuracy. In our experiments on the resting-state fMRI data of 37 subjects (12 mild cognitive impairment patients; 25 healthy normal controls) with a cross-validation technique, we demonstrated the validity and effectiveness of the proposed method, showing the best diagnostic accuracy of 89.19% and the sensitivity of 0.9167.
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References
Friston, K.J., Frith, C.D., Liddle, P.F., Frackowiak, R.S.: Functional connectivity: The principal-component analysis of large (PET) data sets. Journal of Cerebral Blood Flow and Metabolism 13, 5–14 (1993)
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press Professional (1990)
Han, S.D., Arfanakis, K., Fleischman, D.A., Leurgans, S.E., Tuminello, E.R., Edmonds, E.C., Bennett, D.A.: Functional connectivity variations in mild cognitive impairment: associations with cognitive function. Journal of the International Neuropsychological Society 18, 39–48 (2012)
Liu, J., Ji, S., Ye, J.: SLEP: Sparse Learning with Efficient Projections. Arizona State University (2009)
Mcintosh, A.R., Grady, C.L., Ungerleider, L.G., Haxby, J.V., Rapoport, S.I., Horwitzl, B.: Network analysis of cortical visual pathways mapped with PET. Journal of Neuroscience 14, 655–666 (1994)
Misra, C., Fan, Y., Davatzikos, C.: Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. NeuroImage 44, 1414–1422 (2009)
Ng, B., Varoquaux, G., Poline, J.-B., Thirion, B.: A novel sparse graphical approach for multimodal brain connectivity inference. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 707–714. Springer, Heidelberg (2012)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1226–1285 (2005)
Rakotomamonjy, A.: Variable selection using SVM based criteria. Journal of Machine Learning Research 3, 1357–1370 (2003)
Supekar, K., Menon, V., Rubin, D., Musen, M., Greicius, M.D.: Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. PLoS Computational Biology 4, e1000100 (2008)
Thomann, P.A., Schläfer, C., Seidl, U., Santos, V.D., Essig, M., Schröder, J.: The cerebellum in mild cognitive impairment and Alzheimer’s disease - a structural MRI study. Journal of Psychiatric Research 42(14), 1198–1202 (2008)
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)
Van Dijk, K.R.A., Hedden, T., Venkataraman, A., Evans, K.C., Lazar, S.W., Buckner, R.L.: Intrinsic functional connectivity as a tool for human connectomics: Theory, properties and optimization. Journal of Neurophysiology 103, 297–321 (2010)
Wang, Z., Nie, B., Li, D., Zhao, Z., Han, Y.: 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., Wang, L., Shen, D.: Constrained sparse functional connectivity networks for MCI classification. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 212–219. Springer, Heidelberg (2012)
Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society Series B 68(1), 49–67 (2006)
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Suk, HI., Wee, CY., Shen, D. (2013). Discriminative Group Sparse Representation for Mild Cognitive Impairment Classification. In: Wu, G., Zhang, D., Shen, D., Yan, P., Suzuki, K., Wang, F. (eds) Machine Learning in Medical Imaging. MLMI 2013. Lecture Notes in Computer Science, vol 8184. Springer, Cham. https://doi.org/10.1007/978-3-319-02267-3_17
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DOI: https://doi.org/10.1007/978-3-319-02267-3_17
Publisher Name: Springer, Cham
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