Abstract
Mild cognitive impairment (MCI) patients are at a high risk of turning into Alzheimer’s disease (AD) within years. But it is known that not all MCI patients will progress to AD. Therefore, it is of great interest to accurately diagnose whether a MCI patient will convert to AD (namely MCI converter; MCI-C) or not (namely MCI non-converter; MCI-NC), for early diagnosis and proper treatment. In this paper, we propose a multi-task sparse representation classifier to discriminate between MCI-C and MCI-NC utilizing longitudinal neuroimaging data. Unlike the previous methods that explicitly combined the longitudinal information in a feature domain, thus requiring the same number of measurements in time, the proposed method is not limited to the availability of the data. Specifically, by means of multi-task learning, we impose a group constraint that the same training samples, ideally belonging to the same class, are used to represent the longitudinal feature vectors across time points. Then we utilize a sparse representation classifier for label decision. From a machine learning perspective, the proposed method can be considered as the combination of the generative and discriminative methods, which are known to be effective in classification enhancement. In our experiments on magnetic resonance brain images of 349 MCI subjects (164 MCI-C and 185 MCI-NC) from ADNI database, we demonstrate the validity of the proposed method, which also outperforms the competing methods.
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References
Davatzikos, C., et al.: Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiology of Aging 32(12), 2322.e19–2322.e27 (2011)
Zhou, L., et al.: Hierarchical Anatomical Brain Networks for MCI Prediction: Revisiting Volumetric Measures. PLoS ONE 6(7) (2011)
Hinrichs, C., et al.: Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population. NeuroImage 55(2), 574–589 (2011)
Li, Y., et al.: Discriminant analysis of longitudinal cortical thickness changes in Alzheimer’s disease using dynamic and network features. Neurobiology of Aging 33(2), 427.e15–427.e30 (2012)
Zhang, D., Shen, D.: Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers. PLoS ONE 7(3) (2012)
Xiao-Tong, Y., Shuicheng, Y.: Visual classification with multi-task joint sparse representation. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2010)
Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging 17(1), 87–97 (1998)
Wang, Y., Nie, J., Yap, P.-T., Shi, F., Guo, L., Shen, D.: Robust deformable-surface-based skull-stripping for large-scale studies. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 635–642. Springer, Heidelberg (2011)
Kabani, N., et al.: A 3D atlas of the human brain. NeuroImage 7, S717 (1998)
Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 68(1), 9 (2006)
Bishop, C., Lasserre, J.: Generative or Discriminative? Getting the Best of Both Worlds
Wright, J., et al.: Robust Face Recognition via Sparse Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(2), 210–227 (2009)
Cuingnet, R., et al.: Automatic classification of patients with Alzheimer’s disease from structural MRI: A comparison of ten methods using the ADNI database. NeuroImage 56(2), 766–781 (2011)
Zhang, D., et al.: Multimodal classification of Alzheimer’s disease and mild cognitive impairment. NeuroImage 55(3), 856–867 (2011)
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Liu, M., Suk, HI., Shen, D. (2013). Multi-task Sparse Classifier for Diagnosis of MCI Conversion to AD with Longitudinal MR Images. 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_31
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DOI: https://doi.org/10.1007/978-3-319-02267-3_31
Publisher Name: Springer, Cham
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