Identification of individuals with MCI via multimodality connectivity networks.


Journal Article

Alzheimer's disease (AD), is difficult to diagnose due to the subtlety of cognitive impairment. Recent emergence of reliable network characterization techniques based on diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) has made the understanding of neurological disorders at a whole-brain connectivity level possible, providing new avenues for brain classification. Taking a multi-kernel SVM, we attempt to integrate these two imaging modalities for improving classification performance. Our results indicate that the multimodality classification approach performs better than the single modality approach, with statistically significant improvement in accuracy. It was also found that the prefrontal cortex, orbitofrontal cortex, temporal pole, anterior and posterior cingulate gyrus, precuneus, amygdala, thalamus, parahippocampal gyrus and insula regions provided the most discriminant features for classification, in line with the results reported in previous studies. The multimodality classification approach allows more accurate early detection of brain abnormalities with larger sensitivity, and is important for treatment management of potential AD patients.

Full Text

Duke Authors

Cited Authors

  • Wee, C-Y; Yap, P-T; Zhang, D; Denny, K; Wang, L; Shen, D

Published Date

  • 2011

Published In

  • Med Image Comput Comput Assist Interv

Volume / Issue

  • 14 / Pt 2

Start / End Page

  • 277 - 284

PubMed ID

  • 21995039

Pubmed Central ID

  • 21995039

Digital Object Identifier (DOI)

  • 10.1007/978-3-642-23629-7_34


  • eng

Conference Location

  • Germany