High-Order Laplacian Regularized Low-Rank Representation for Multimodal Dementia Diagnosis

Aimei Dong, Zhigang Li, Mingliang Wang, Dinggang Shen, Mingxia Liu

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Multimodal heterogeneous data, such as structural magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF), are effective in improving the performance of automated dementia diagnosis by providing complementary information on degenerated brain disorders, such as Alzheimer's prodromal stage, i.e., mild cognitive impairment. Effectively integrating multimodal data has remained a challenging problem, especially when these heterogeneous data are incomplete due to poor data quality and patient dropout. Besides, multimodal data usually contain noise information caused by different scanners or imaging protocols. The existing methods usually fail to well handle these heterogeneous and noisy multimodal data for automated brain dementia diagnosis. To this end, we propose a high-order Laplacian regularized low-rank representation method for dementia diagnosis using block-wise missing multimodal data. The proposed method was evaluated on 805 subjects (with incomplete MRI, PET, and CSF data) from the real Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Experimental results suggest the effectiveness of our method in three tasks of brain disease classification, compared with the state-of-the-art methods.

Original languageEnglish
Article number634124
JournalFrontiers in Neuroscience
Publication statusPublished - 2021 Mar 12

Bibliographical note

Publisher Copyright:
© Copyright © 2021 Dong, Li, Wang, Shen and Liu.


  • classification
  • dementia
  • high-order
  • incomplete heterogeneous data
  • low-rank representation

ASJC Scopus subject areas

  • General Neuroscience


Dive into the research topics of 'High-Order Laplacian Regularized Low-Rank Representation for Multimodal Dementia Diagnosis'. Together they form a unique fingerprint.

Cite this