Classification of Alzheimer's disease based on hippocampal multivariate morphometry statistics

Weimin Zheng, Honghong Liu, Zhigang Li, Kuncheng Li, Yalin Wang, Bin Hu, Qunxi Dong, Zhiqun Wang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Background: Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive cognitive decline, and mild cognitive impairment (MCI) is associated with a high risk of developing AD. Hippocampal morphometry analysis is believed to be the most robust magnetic resonance imaging (MRI) markers for AD and MCI. Multivariate morphometry statistics (MMS), a quantitative method of surface deformations analysis, is confirmed to have strong statistical power for evaluating hippocampus. Aims: We aimed to test whether surface deformation features in hippocampus can be employed for early classification of AD, MCI, and healthy controls (HC). Methods: We first explored the differences in hippocampus surface deformation among these three groups by using MMS analysis. Additionally, the hippocampal MMS features of selective patches and support vector machine (SVM) were used for the binary classification and triple classification. Results: By the results, we identified significant hippocampal deformation among the three groups, especially in hippocampal CA1. In addition, the binary classification of AD/HC, MCI/HC, AD/MCI showed good performances, and area under curve (AUC) of triple-classification model achieved 0.85. Finally, positive correlations were found between the hippocampus MMS features and cognitive performances. Conclusions: The study revealed significant hippocampal deformation among AD, MCI, and HC. Additionally, we confirmed that hippocampal MMS can be used as a sensitive imaging biomarker for the early diagnosis of AD at the individual level.

Original languageEnglish (US)
Pages (from-to)2457-2468
Number of pages12
JournalCNS Neuroscience and Therapeutics
Issue number9
StatePublished - Sep 2023
Externally publishedYes


  • AD patient stratification
  • SVM classification
  • computer-aided diagnosis
  • hippocampal morphometry
  • patch-based feature selection

ASJC Scopus subject areas

  • Pharmacology
  • Psychiatry and Mental health
  • Physiology (medical)
  • Pharmacology (medical)


Dive into the research topics of 'Classification of Alzheimer's disease based on hippocampal multivariate morphometry statistics'. Together they form a unique fingerprint.

Cite this