Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers

Daoqiang Zhang, Dinggang Shen

Research output: Contribution to journalArticle

147 Citations (Scopus)

Abstract

Accurate prediction of clinical changes of mild cognitive impairment (MCI) patients, including both qualitative change (i.e., conversion to Alzheimer's disease (AD)) and quantitative change (i.e., cognitive scores) at future time points, is important for early diagnosis of AD and for monitoring the disease progression. In this paper, we propose to predict future clinical changes of MCI patients by using both baseline and longitudinal multimodality data. To do this, we first develop a longitudinal feature selection method to jointly select brain regions across multiple time points for each modality. Specifically, for each time point, we train a sparse linear regression model by using the imaging data and the corresponding clinical scores, with an extra 'group regularization' to group the weights corresponding to the same brain region across multiple time points together and to allow for selection of brain regions based on the strength of multiple time points jointly. Then, to further reflect the longitudinal changes on the selected brain regions, we extract a set of longitudinal features from the original baseline and longitudinal data. Finally, we combine all features on the selected brain regions, from different modalities, for prediction by using our previously proposed multi-kernel SVM. We validate our method on 88 ADNI MCI subjects, with both MRI and FDG-PET data and the corresponding clinical scores (i.e., MMSE and ADAS-Cog) at 5 different time points. We first predict the clinical scores (MMSE and ADAS-Cog) at 24-month by using the multimodality data at previous time points, and then predict the conversion of MCI to AD by using the multimodality data at time points which are at least 6-month ahead of the conversion. The results on both sets of experiments show that our proposed method can achieve better performance in predicting future clinical changes of MCI patients than the conventional methods.

Original languageEnglish
Article numbere33182
JournalPLoS One
Volume7
Issue number3
DOIs
Publication statusPublished - 2012 Mar 22
Externally publishedYes

Fingerprint

Biomarkers
biomarkers
Brain
brain
Alzheimer disease
Alzheimer Disease
prediction
early diagnosis
Linear Models
selection methods
disease course
Linear regression
Magnetic resonance imaging
Feature extraction
methodology
Cognitive Dysfunction
image analysis
Imaging techniques
Monitoring
monitoring

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. / Zhang, Daoqiang; Shen, Dinggang.

In: PLoS One, Vol. 7, No. 3, e33182, 22.03.2012.

Research output: Contribution to journalArticle

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