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

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

Research output: Contribution to journalArticle

2 Citations (Scopus)


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 (ADMM). Experimental results on both synthetic and real data validate the effectiveness of our proposed algorithm.

Original languageEnglish
JournalIEEE Transactions on Medical Imaging
Publication statusAccepted/In press - 2018 Jan 1


  • Brain modeling
  • Cognitive ability
  • Computational modeling
  • Data models
  • Infant brain development
  • Longitudinal analysis
  • Magnetic resonance imaging
  • Multi-view learning
  • Pediatrics
  • Predictive models
  • Task analysis

ASJC Scopus subject areas

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

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