Learning-based estimation of functional correlation tensors in white matter for early diagnosis of mild cognitive impairment

Lichi Zhang; Han Zhang; Xiaobo Chen; Qian Wang; Pew Thian Yap; Dinggang Shen

doi:10.1007/978-3-319-67434-6_8

Learning-based estimation of functional correlation tensors in white matter for early diagnosis of mild cognitive impairment

Lichi Zhang, Han Zhang, Xiaobo Chen, Qian Wang, Pew Thian Yap, Dinggang Shen

Department of Brain and Cognitive Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

It has been recently demonstrated that the local BOLD signals in resting-state fMRI (rs-fMRI) can be captured for the white matter (WM) by functional correlation tensors (FCTs). FCTs provide similar orientation information as diffusion tensors (DTs), and also functional information concerning brain dynamics. However, FCTs are susceptible to noise due to the low signal-to-noise ratio nature of WM BOLD signals. Here we introduce a robust FCT estimation method to facilitate individualized diagnosis. First, we develop a noise-tolerating patch-based approach to measure spatiotemporal correlations of local BOLD signals. Second, it is also enhanced by DTs predicted from the input rs-fMRI using a learning-based regression model. We evaluate our trained regressor using the high-resolution HCP dataset. The regressor is then applied to estimate the robust FCTs for subjects in the ADNI2 dataset. We demonstrate for the first time the disease diagnostic value of robust FCTs.

Original language	English
Title of host publication	Patch-Based Techniques in Medical Imaging - 3rd International Workshop, Patch-MI 2017 Held in Conjunction with MICCAI 2017, Proceedings
Editors	Yiqiang Zhan, Wenjia Bai, Guorong Wu, Pierrick Coupe, Brent C. Munsell, Gerard Sanroma
Publisher	Springer Verlag
Pages	65-73
Number of pages	9
ISBN (Print)	9783319674339
DOIs	https://doi.org/10.1007/978-3-319-67434-6_8
Publication status	Published - 2017
Event	3rd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2017 held in conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada Duration: 2017 Sep 14 → 2017 Sep 14

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10530 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	3rd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2017 held in conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Country	Canada
City	Quebec City
Period	17/9/14 → 17/9/14

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-67434-6_8

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Cite this

Zhang, L., Zhang, H., Chen, X., Wang, Q., Yap, P. T., & Shen, D. (2017). Learning-based estimation of functional correlation tensors in white matter for early diagnosis of mild cognitive impairment. In Y. Zhan, W. Bai, G. Wu, P. Coupe, B. C. Munsell, & G. Sanroma (Eds.), Patch-Based Techniques in Medical Imaging - 3rd International Workshop, Patch-MI 2017 Held in Conjunction with MICCAI 2017, Proceedings (pp. 65-73). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10530 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67434-6_8

Learning-based estimation of functional correlation tensors in white matter for early diagnosis of mild cognitive impairment. / Zhang, Lichi; Zhang, Han; Chen, Xiaobo; Wang, Qian; Yap, Pew Thian; Shen, Dinggang.

Patch-Based Techniques in Medical Imaging - 3rd International Workshop, Patch-MI 2017 Held in Conjunction with MICCAI 2017, Proceedings. ed. / Yiqiang Zhan; Wenjia Bai; Guorong Wu; Pierrick Coupe; Brent C. Munsell; Gerard Sanroma. Springer Verlag, 2017. p. 65-73 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10530 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, L, Zhang, H, Chen, X, Wang, Q, Yap, PT & Shen, D 2017, Learning-based estimation of functional correlation tensors in white matter for early diagnosis of mild cognitive impairment. in Y Zhan, W Bai, G Wu, P Coupe, BC Munsell & G Sanroma (eds), Patch-Based Techniques in Medical Imaging - 3rd International Workshop, Patch-MI 2017 Held in Conjunction with MICCAI 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10530 LNCS, Springer Verlag, pp. 65-73, 3rd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2017 held in conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017, Quebec City, Canada, 17/9/14. https://doi.org/10.1007/978-3-319-67434-6_8

Zhang L, Zhang H, Chen X, Wang Q, Yap PT, Shen D. Learning-based estimation of functional correlation tensors in white matter for early diagnosis of mild cognitive impairment. In Zhan Y, Bai W, Wu G, Coupe P, Munsell BC, Sanroma G, editors, Patch-Based Techniques in Medical Imaging - 3rd International Workshop, Patch-MI 2017 Held in Conjunction with MICCAI 2017, Proceedings. Springer Verlag. 2017. p. 65-73. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-67434-6_8

Zhang, Lichi ; Zhang, Han ; Chen, Xiaobo ; Wang, Qian ; Yap, Pew Thian ; Shen, Dinggang. / Learning-based estimation of functional correlation tensors in white matter for early diagnosis of mild cognitive impairment. Patch-Based Techniques in Medical Imaging - 3rd International Workshop, Patch-MI 2017 Held in Conjunction with MICCAI 2017, Proceedings. editor / Yiqiang Zhan ; Wenjia Bai ; Guorong Wu ; Pierrick Coupe ; Brent C. Munsell ; Gerard Sanroma. Springer Verlag, 2017. pp. 65-73 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "It has been recently demonstrated that the local BOLD signals in resting-state fMRI (rs-fMRI) can be captured for the white matter (WM) by functional correlation tensors (FCTs). FCTs provide similar orientation information as diffusion tensors (DTs), and also functional information concerning brain dynamics. However, FCTs are susceptible to noise due to the low signal-to-noise ratio nature of WM BOLD signals. Here we introduce a robust FCT estimation method to facilitate individualized diagnosis. First, we develop a noise-tolerating patch-based approach to measure spatiotemporal correlations of local BOLD signals. Second, it is also enhanced by DTs predicted from the input rs-fMRI using a learning-based regression model. We evaluate our trained regressor using the high-resolution HCP dataset. The regressor is then applied to estimate the robust FCTs for subjects in the ADNI2 dataset. We demonstrate for the first time the disease diagnostic value of robust FCTs.",

author = "Lichi Zhang and Han Zhang and Xiaobo Chen and Qian Wang and Yap, {Pew Thian} and Dinggang Shen",

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AB - It has been recently demonstrated that the local BOLD signals in resting-state fMRI (rs-fMRI) can be captured for the white matter (WM) by functional correlation tensors (FCTs). FCTs provide similar orientation information as diffusion tensors (DTs), and also functional information concerning brain dynamics. However, FCTs are susceptible to noise due to the low signal-to-noise ratio nature of WM BOLD signals. Here we introduce a robust FCT estimation method to facilitate individualized diagnosis. First, we develop a noise-tolerating patch-based approach to measure spatiotemporal correlations of local BOLD signals. Second, it is also enhanced by DTs predicted from the input rs-fMRI using a learning-based regression model. We evaluate our trained regressor using the high-resolution HCP dataset. The regressor is then applied to estimate the robust FCTs for subjects in the ADNI2 dataset. We demonstrate for the first time the disease diagnostic value of robust FCTs.

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