HFPRM: Hierarchical functional principal regression model for diffusion tensor image bundle statistics

Jingwen Zhang; Chao Huang; Joseph G. Ibrahim; Shaili Jha; Rebecca C. Knickmeyer; John H. Gilmore; Martin Styner; Hongtu Zhu

doi:10.1007/978-3-319-59050-9_38

HFPRM: Hierarchical functional principal regression model for diffusion tensor image bundle statistics

Jingwen Zhang, Chao Huang, Joseph G. Ibrahim, Shaili Jha, Rebecca C. Knickmeyer, John H. Gilmore, Martin Styner, Hongtu Zhu

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

1 Scopus citations

Abstract

Diffusion-weighted magnetic resonance imaging (MRI) provides a unique approach to understand the geometric structure of brain fiber bundles and to delineate the diffusion properties across subjects and time. It can be used to identify structural connectivity abnormalities and helps to diagnose brain-related disorders. The aim of this paper is to develop a novel, robust, and efficient dimensional reduction and regression framework, called hierarchical functional principal regression model (HFPRM), to effectively correlate high-dimensional fiber bundle statistics with a set of predictors of interest, such as age, diagnosis status, and genetic markers. The three key novelties of HFPRM include the simultaneous analysis of a large number of fiber bundles, the disentanglement of global and individual latent factors that characterizes between-tract correlation patterns, and a bi-level analysis on the predictor effects. Simulations are conducted to evaluate the finite sample performance of HFPRM. We have also applied HFPRM to a genomewide association study to explore important genetic variants in neonatal white matter development.

Original language	English (US)
Title of host publication	Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings
Editors	Hongtu Zhu, Marc Niethammer, Martin Styner, Hongtu Zhu, Dinggang Shen, Pew-Thian Yap, Stephen Aylward, Ipek Oguz
Publisher	Springer Verlag
Pages	478-489
Number of pages	12
ISBN (Print)	9783319590493
DOIs	https://doi.org/10.1007/978-3-319-59050-9_38
State	Published - 2017
Event	25th International Conference on Information Processing in Medical Imaging, IPMI 2017 - Boone, United States Duration: Jun 25 2017 → Jun 30 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10265 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	25th International Conference on Information Processing in Medical Imaging, IPMI 2017
Country/Territory	United States
City	Boone
Period	6/25/17 → 6/30/17

Keywords

Factor analysis
Fiber bundle statistics
Functional principal component analysis
Imaging genetics
Varying coefficient model

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-59050-9_38

Cite this

Zhang, J., Huang, C., Ibrahim, J. G., Jha, S., Knickmeyer, R. C., Gilmore, J. H., Styner, M., & Zhu, H. (2017). HFPRM: Hierarchical functional principal regression model for diffusion tensor image bundle statistics. In H. Zhu, M. Niethammer, M. Styner, H. Zhu, D. Shen, P.-T. Yap, S. Aylward, & I. Oguz (Eds.), Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings (pp. 478-489). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10265 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-59050-9_38

HFPRM: Hierarchical functional principal regression model for diffusion tensor image bundle statistics. / Zhang, Jingwen; Huang, Chao; Ibrahim, Joseph G. et al.
Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. ed. / Hongtu Zhu; Marc Niethammer; Martin Styner; Hongtu Zhu; Dinggang Shen; Pew-Thian Yap; Stephen Aylward; Ipek Oguz. Springer Verlag, 2017. p. 478-489 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10265 LNCS).

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

Zhang, J, Huang, C, Ibrahim, JG, Jha, S, Knickmeyer, RC, Gilmore, JH, Styner, M & Zhu, H 2017, HFPRM: Hierarchical functional principal regression model for diffusion tensor image bundle statistics. in H Zhu, M Niethammer, M Styner, H Zhu, D Shen, P-T Yap, S Aylward & I Oguz (eds), Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10265 LNCS, Springer Verlag, pp. 478-489, 25th International Conference on Information Processing in Medical Imaging, IPMI 2017, Boone, United States, 6/25/17. https://doi.org/10.1007/978-3-319-59050-9_38

Zhang J, Huang C, Ibrahim JG, Jha S, Knickmeyer RC, Gilmore JH et al. HFPRM: Hierarchical functional principal regression model for diffusion tensor image bundle statistics. In Zhu H, Niethammer M, Styner M, Zhu H, Shen D, Yap PT, Aylward S, Oguz I, editors, Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. Springer Verlag. 2017. p. 478-489. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-59050-9_38

Zhang, Jingwen ; Huang, Chao ; Ibrahim, Joseph G. et al. / HFPRM : Hierarchical functional principal regression model for diffusion tensor image bundle statistics. Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. editor / Hongtu Zhu ; Marc Niethammer ; Martin Styner ; Hongtu Zhu ; Dinggang Shen ; Pew-Thian Yap ; Stephen Aylward ; Ipek Oguz. Springer Verlag, 2017. pp. 478-489 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Diffusion-weighted magnetic resonance imaging (MRI) provides a unique approach to understand the geometric structure of brain fiber bundles and to delineate the diffusion properties across subjects and time. It can be used to identify structural connectivity abnormalities and helps to diagnose brain-related disorders. The aim of this paper is to develop a novel, robust, and efficient dimensional reduction and regression framework, called hierarchical functional principal regression model (HFPRM), to effectively correlate high-dimensional fiber bundle statistics with a set of predictors of interest, such as age, diagnosis status, and genetic markers. The three key novelties of HFPRM include the simultaneous analysis of a large number of fiber bundles, the disentanglement of global and individual latent factors that characterizes between-tract correlation patterns, and a bi-level analysis on the predictor effects. Simulations are conducted to evaluate the finite sample performance of HFPRM. We have also applied HFPRM to a genomewide association study to explore important genetic variants in neonatal white matter development.",

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AB - Diffusion-weighted magnetic resonance imaging (MRI) provides a unique approach to understand the geometric structure of brain fiber bundles and to delineate the diffusion properties across subjects and time. It can be used to identify structural connectivity abnormalities and helps to diagnose brain-related disorders. The aim of this paper is to develop a novel, robust, and efficient dimensional reduction and regression framework, called hierarchical functional principal regression model (HFPRM), to effectively correlate high-dimensional fiber bundle statistics with a set of predictors of interest, such as age, diagnosis status, and genetic markers. The three key novelties of HFPRM include the simultaneous analysis of a large number of fiber bundles, the disentanglement of global and individual latent factors that characterizes between-tract correlation patterns, and a bi-level analysis on the predictor effects. Simulations are conducted to evaluate the finite sample performance of HFPRM. We have also applied HFPRM to a genomewide association study to explore important genetic variants in neonatal white matter development.

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HFPRM: Hierarchical functional principal regression model for diffusion tensor image bundle statistics

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Keywords

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