research-article

Heterogeneous data fusion for alzheimer's disease study

Authors:

Gene Alexander,

Eric ReimanAuthors Info & Claims

KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1025 - 1033

https://doi.org/10.1145/1401890.1402012

Published: 24 August 2008 Publication History

Abstract

Effective diagnosis of Alzheimer's disease (AD) is of primary importance in biomedical research. Recent studies have demonstrated that neuroimaging parameters are sensitive and consistent measures of AD. In addition, genetic and demographic information have also been successfully used for detecting the onset and progression of AD. The research so far has mainly focused on studying one type of data source only. It is expected that the integration of heterogeneous data (neuroimages, demographic, and genetic measures) will improve the prediction accuracy and enhance knowledge discovery from the data, such as the detection of biomarkers. In this paper, we propose to integrate heterogeneous data for AD prediction based on a kernel method. We further extend the kernel framework for selecting features (biomarkers) from heterogeneous data sources. The proposed method is applied to a collection of MRI data from 59 normal healthy controls and 59 AD patients. The MRI data are pre-processed using tensor factorization. In this study, we treat the complementary voxel-based data and region of interest (ROI) data from MRI as two data sources, and attempt to integrate the complementary information by the proposed method. Experimental results show that the integration of multiple data sources leads to a considerable improvement in the prediction accuracy. Results also show that the proposed algorithm identifies biomarkers that play more significant roles than others in AD diagnosis.

References

[1]

G. Alexander and et al. Regional network of MRI gray matter volume in healthy aging. Neuroreport, 17:951--956, 2006.

[2]

G. Alexander and J. Moeller. Application of the scaled subprofile model to functional imaging in neuropsychiatric disorder: a principal component approach to modeling brain function in disease. Human Brain Mapping, 2(1-2):79--94, 2004.

[3]

G. Alexander and E. Reiman. Neuroimaging. M.F. Weiner, A.M. Lipton (eds.). The Dementias: Diagnosis, Treatment and Research, 3rd edition, 2003.

[4]

R. G. Bachrach, A. Navot, and N. Tishby. Margin based feature selection - theory and algorithms. In International Conference on Machine Learning (ICML), 2004.

Digital Library

[5]

B. Bader and T. Kolda. MATLAB Tensor Toolbox Version 2.2. http://csmr.ca.sandia.gov/~tgkolda/TensorToolbox/, January 2007.

[6]

G. C. Cawley and N. L. C. Talbot. Gene selection in cancer classification using sparse logistic regression with bayesian regularization. BIOINFORMATICS, 22:2348--2355, 2006.

Digital Library

[7]

C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

[8]

K. Chen, E. Reiman, G. Alexander, D. Bandy, R. Renaut, W. Crum, N. Fox, and M. Rossor. An automated algorithm for the computation of brain volume change from sequential MRI's using an iterative principal component analysis and its evaluation for the assessment of whole brain atrophy rates in patients with probable Alzheimer's disease. Neuroimage, 22(1):134--143, 2004.

[9]

M. Davison. Multidimensional Scaling. New York: Wiley, 1983.

[10]

C. Ding and J. Ye. 2-Dimensional singular value decomposition for 2D maps and images. In Proceedings of the Fifth SIAM International Conference on Data Mining (SDM), pages 24--34, 2005.

[11]

G. Forman. An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research, 3:1289--1305, 2003.

Digital Library

[12]

G. Fung and J. Stoeckel. Svm feature selection for classification of spect images of alzheimer disease using spatial information. Knowledge and Information Systems, 11:243--258, 2007.

Digital Library

[13]

I. Guyon and A. Elisseeff. An introduction to variable and feature selection. Journal of Machine Learning Research, 3:1157--1182, 2003.

Digital Library

[14]

I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene selection for cancer classification using support vector machines. Mach. Learn., 46(1-3):389--422, 2002.

Digital Library

[15]

X. He, D. Cai, and P. Niyogi. Laplacian score for feature selection. In Advances in Neural Information Processing Systems 18. MIT Press, 2005.

Digital Library

[16]

I. T. Jolliffe. Principal Component Analysis. Springer-Verlag, New York, 1986.

[17]

S. S. Keerthi. Generalized lars as an effective feature selection tool for text classification with svms. In International Conference on Machine Learning (ICML), 2005.

Digital Library

[18]

K. Kira and L. Rendell. A practical approach to feature selection. In Sleeman and P. Edwards, editors, ICML '92: Proceedings of the Ninth International Conference on Machine Learning, pages 249--256. Morgan Kaufmann, 1992.

Digital Library

[19]

T. Kolda. Orthogonal tensor decompositions. SIAM J. Matrix Anal. Appl., 23(1):243--255, 2001.

Digital Library

[20]

I. Kononenko. Estimating attributes: Analysis and extensions of relief. In ECML, page 171--82, 1994.

Digital Library

[21]

J. Krasaski and et al. Relation of medial temporal lobe volumes to aget and memory function in nondemented adults with down's syndrome: implications for the prodromal phase of Alzheimer's disease. American Journal of Psychiatry, 159:74--81, 2002.

[22]

G. Lanckriet, T. D. Bie, N. Cristianini, M. Jordan, and W. Noble. A statistical framework for genomic data fusion. Bioinformatics, 20(16):2626--2635, 2004.

Digital Library

[23]

G. Lanckriet, N. Cristianini, P. Bartlett, L. E. Ghaoui, and M. I. Jordan. Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research, 5:27--72, 2004.

Digital Library

[24]

O. Lange, A. Meyer-Baese, M. Hurdal, and S. Foo. A comparison between neural and fuzzy cluster analysis techniques for functional MRI. Biomedical Signal Processing and Control, 1(3):243--252, 2006.

[25]

L. Lathauwer, B. Moor, and J. Vandewalle. On the best Rank-1 and Rank-(R1,R2,.,RN) approximation of higher-order tensors. SIAM J. Matrix Anal. Appl., 21(4):1324--1342, 2000.

Digital Library

[26]

D. Leibovici and R. Sabatier. A singular value decomposition of k-way array for a principal component analysis of multiway data, PTA-k. Linear Algebra and Its Applications, 269:307--329, 1998.

[27]

T. Li, C. Zhang, and M. Ogihara. A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. BIOINFORMATICS, 20:2429--2437, 2004.

Digital Library

[28]

Y. Li, C. Campbell, and M. Tipping. Bayesian automatic relevance determination algorithms for classifying gene expression data. BIOINFORMATICS, 18:1332--1339, 2002.

[29]

H. Liu and H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Boston: Kluwer Academic Publishers, 1998.

Digital Library

[30]

H. Liu and L. Yu. Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17:491--502, 2005.

Digital Library

[31]

H. Matsuda. Role of neuroimaging in Alzheimer's disease, with emphasis on brain perfusion SPECT. Journal of Nuclear Medicine, 48(8):1289--1300, 2007.

[32]

S. Molchan. The Alzheimer's Disease Neuroimaging Initiative. Business Briefing: US Neurology Review, pages 30--32, 2005.

[33]

A. Ng, M. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In The 14th Advances in Neural Information Processing Systems (NIPS), 2001.

[34]

K. Nishino, Y. Sato, and K. Ikeuchi. Eigen-texture method: appearance compression based on 3d model. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 618--624, 1999.

[35]

D. Pokrajac, V. Megalooikonomou, A. Lazarevic, D. Kontos, and Z. Obradovic. Applying spatial distribution analysis techniques to classification of 3d medical images. Artificial Intelligence in Medicine, 33(3):261--280, 2005.

Digital Library

[36]

E. Reiman, R. Caselli, G. Alexander, and K. Chen. Tracking the decline in cerebral glucose metabolism in persons and laboratory animals at genetic risk for Alzheimer's disease. Clinical Neuroscience Research, 1:194--206, 2001.

[37]

B. Schökopf and A. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. MIT Press, 2002.

[38]

J. Shawe-Taylor and N. Cristianini. Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.

Digital Library

[39]

M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1):71--86, 1991.

Digital Library

[40]

N. Tzourio-Mazoyer and et al. Automated anatomical labelling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single subject brain. Neuroimage, 15:273--289, 2002.

[41]

M. Vasilescu and D. Terzopoulos. Multilinear analysis of image ensembles: Tensorfaces. In Proceedings of the European Conference on Computer Vision (ECCV), pages 447--460, 2002.

Digital Library

[42]

M. Vasilescu and D. Terzopoulos. Tensortextures: multilinear image-based rendering. ACM Trans. Graph., 23(3):336--342, 2004.

Digital Library

[43]

H. Wang, Q. Wu, L. Shi, Y. Yu, and N. Ahuja. Out-of-core tensor approximation of multi-dimensional matrices of visual data. ACM Trans. Graph., 24(3):527--535, 2005.

Digital Library

[44]

K. Worsley and K. Friston. Analysis of fMRI time series revisited-again. NeuroImage, 2:173--181, 1995.

[45]

R. Wrede. Introduction to Vector and Tensor Analysis. New York: Wiley, 1963.

[46]

J. Ye. Generalized low rank approximations of matrices. Machine Learning, 61:167--191, 2005.

Digital Library

[47]

J. Ye, J. Chen, and S. Ji. Discriminant kernel and regularization parameter learning via semidefinite programming. In Proceedings of the twenty-fourth International Conference on Machine Learning, pages 1095--1102, 2007.

Digital Library

[48]

J. Ye, S. Ji, and J. Chen. Learning the kernel matrix in discriminant analysis via quadratically constrained quadratic programming. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 854--863, 2007.

Digital Library

[49]

T. Zhang and R. Ando. Analysis of spectral kernel design based semi-supervised learning. In Advances in Neural Information Processing Systems 18, pages 1601--1608, 2006.

[50]

Z. Zhao and H. Liu. Spectral feature selection for supervised and unsupervised learning. In International Conference on Machine Learning (ICML), 2007.

Digital Library

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Index Terms

Heterogeneous data fusion for alzheimer's disease study
1. Applied computing
  1. Life and medical sciences
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2008

1116 pages

ISBN:9781605581934

DOI:10.1145/1401890

General Chair:
Ying Li
Microsoft adCenter Labs
,
Program Chairs:
Bing Liu
University of Illinois at Chicago
,
Sunita Sarawagi
Indian Institute of Technology, Bombay

Copyright © 2008 ACM.

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Published: 24 August 2008

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August 24 - 27, 2008

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KDD '08 Paper Acceptance Rate 118 of 593 submissions, 20%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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https://doi.org/10.1177/25424823241284464
Ahanger AAalam SAssad AMacha MBhat M(2024)Alzhinet: an explainable self-attention based classification model to detect Alzheimer from 3D volumetric MRI dataInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02377-wOnline publication date: 2-Jun-2024
https://doi.org/10.1007/s13198-024-02377-w
Chamma AEngemann DThirion BOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Statistically valid variable importance assessment through conditional permutationsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669081(67662-67685)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3669081
Garg NChoudhry MBodade R(2023)A review on Alzheimer’s disease classification from normal controls and mild cognitive impairment using structural MR imagesJournal of Neuroscience Methods10.1016/j.jneumeth.2022.109745384(109745)Online publication date: Jan-2023
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