Cognitive Assessment Prediction in Alzheimer’s Disease by Multi-Layer Multi-Target Regression

Xiaoqian Wang, Xiantong Zhen, Quanzheng Li, Dinggang Shen, Heng Huang

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


Accurate and automatic prediction of cognitive assessment from multiple neuroimaging biomarkers is crucial for early detection of Alzheimer’s disease. The major challenges arise from the nonlinear relationship between biomarkers and assessment scores and the inter-correlation among them, which have not yet been well addressed. In this paper, we propose multi-layer multi-target regression (MMR) which enables simultaneously modeling intrinsic inter-target correlations and nonlinear input-output relationships in a general compositional framework. Specifically, by kernelized dictionary learning, the MMR can effectively handle highly nonlinear relationship between biomarkers and assessment scores; by robust low-rank linear learning via matrix elastic nets, the MMR can explicitly encode inter-correlations among multiple assessment scores; moreover, the MMR is flexibly and allows to work with non-smooth ℓ2,1-norm loss function, which enables calibration of multiple targets with disparate noise levels for more robust parameter estimation. The MMR can be efficiently solved by an alternating optimization algorithm via gradient descent with guaranteed convergence. The MMR has been evaluated by extensive experiments on the ADNI database with MRI data, and produced high accuracy surpassing previous regression models, which demonstrates its great effectiveness as a new multi-target regression model for clinical multivariate prediction.

Original languageEnglish
Pages (from-to)285-294
Number of pages10
Issue number3-4
Publication statusPublished - 2018 Oct 1
Externally publishedYes

Bibliographical note

Funding Information:
Acknowledgements This work was partially supported by the following grants: NSF-DBI 1356628, NSF-IIS 1633753, NIH R01 AG049371.

Publisher Copyright:
© 2018, Springer Science+Business Media, LLC, part of Springer Nature.


  • Alzheimer’s disease
  • Calibration
  • Multi-target regression
  • Nonlinear regression
  • Robust low-rank learning

ASJC Scopus subject areas

  • Software
  • General Neuroscience
  • Information Systems


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