Infant Brain Development Prediction With Latent Partial Multi-View Representation Learning

Changqing Zhang; Ehsan Adeli; Zhengwang Wu; Gang Li; Weili Lin; Dinggang Shen

doi:10.1109/TMI.2018.2874964

Infant Brain Development Prediction With Latent Partial Multi-View Representation Learning

Changqing Zhang, Ehsan Adeli, Zhengwang Wu, Gang Li, Weili Lin, Dinggang Shen

Department of Brain and Cognitive Engineering

Research output: Contribution to journal › Article › peer-review

19 Citations (Scopus)

Abstract

The early postnatal period witnesses rapid and dynamic brain development. However, the relationship between brain anatomical structure and cognitive ability is still unknown. Currently, there is no explicit model to characterize this relationship in the literature. In this paper, we explore this relationship by investigating the mapping between morphological features of the cerebral cortex and cognitive scores. To this end, we introduce a multi-view multi-task learning approach to intuitively explore complementary information from different time-points and handle the missing data issue in longitudinal studies simultaneously. Accordingly, we establish a novel model, latent partial multi-view representation learning. Our approach regards data from different time-points as different views and constructs a latent representation to capture the complementary information from incomplete time-points. The latent representation explores the complementarity across different time-points and improves the accuracy of prediction. The minimization problem is solved by the alternating direction method of multipliers. Experimental results on both synthetic and real data validate the effectiveness of our proposed algorithm.

Original language	English
Article number	8487012
Pages (from-to)	909-918
Number of pages	10
Journal	IEEE Transactions on Medical Imaging
Volume	38
Issue number	4
DOIs	https://doi.org/10.1109/TMI.2018.2874964
Publication status	Published - 2019 Apr

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61602337. This work was also supported by NIH under Grants CA206100, MH100217, MH107815, MH108914, MH110274, MH116225, MH117943, AA026762 and the BCP Grant 1U01MH110274.

Funding Information:
Manuscript received August 3, 2018; revised September 29, 2018; accepted October 3, 2018. Date of publication October 9, 2018; date of current version April 2, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61602337, in part by NIH under Grants CA206100, MH100217, MH107815, MH108914, MH110274, MH116225, MH117943, and AA026762, and in part by BCP under Grant 1U01MH110274. (Corresponding author: Dinggang Shen.) C. Zhang is with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA, and also with the College of Intelligence and Computing, Tianjin University, Tianjin 300350, China (e-mail: zhangchangqing@tju.edu.cn).

Publisher Copyright:
© 1982-2012 IEEE.

Keywords

Infant brain development
cognitive ability
longitudinal analysis
multi-view learning

ASJC Scopus subject areas

Software
Radiological and Ultrasound Technology
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/TMI.2018.2874964

Cite this

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title = "Infant Brain Development Prediction With Latent Partial Multi-View Representation Learning",

abstract = "The early postnatal period witnesses rapid and dynamic brain development. However, the relationship between brain anatomical structure and cognitive ability is still unknown. Currently, there is no explicit model to characterize this relationship in the literature. In this paper, we explore this relationship by investigating the mapping between morphological features of the cerebral cortex and cognitive scores. To this end, we introduce a multi-view multi-task learning approach to intuitively explore complementary information from different time-points and handle the missing data issue in longitudinal studies simultaneously. Accordingly, we establish a novel model, latent partial multi-view representation learning. Our approach regards data from different time-points as different views and constructs a latent representation to capture the complementary information from incomplete time-points. The latent representation explores the complementarity across different time-points and improves the accuracy of prediction. The minimization problem is solved by the alternating direction method of multipliers. Experimental results on both synthetic and real data validate the effectiveness of our proposed algorithm.",

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author = "Changqing Zhang and Ehsan Adeli and Zhengwang Wu and Gang Li and Weili Lin and Dinggang Shen",

note = "Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 61602337. This work was also supported by NIH under Grants CA206100, MH100217, MH107815, MH108914, MH110274, MH116225, MH117943, AA026762 and the BCP Grant 1U01MH110274. Funding Information: Manuscript received August 3, 2018; revised September 29, 2018; accepted October 3, 2018. Date of publication October 9, 2018; date of current version April 2, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61602337, in part by NIH under Grants CA206100, MH100217, MH107815, MH108914, MH110274, MH116225, MH117943, and AA026762, and in part by BCP under Grant 1U01MH110274. (Corresponding author: Dinggang Shen.) C. Zhang is with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA, and also with the College of Intelligence and Computing, Tianjin University, Tianjin 300350, China (e-mail: zhangchangqing@tju.edu.cn). Publisher Copyright: {\textcopyright} 1982-2012 IEEE.",

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Infant Brain Development Prediction With Latent Partial Multi-View Representation Learning

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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