Identification of individuals with MCI via multimodality connectivity networks

Chong Yaw Wee, Pew Thian Yap, Daoqiang Zhang, Kevin Denny, Lihong Wang, Dinggang Shen

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Mild cognitive impairment (MCI), often an early stage of 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.

Original languageEnglish
Pages (from-to)277-284
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6892 LNCS
Issue numberPART 2
DOIs
Publication statusPublished - 2011 Oct 11
Externally publishedYes

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Multimodality
Network Connectivity
Brain
Alzheimer's Disease
Cortex
Modality
Imaging
Diffusion tensor imaging
Functional Magnetic Resonance Imaging
Discriminant
Pole
Disorder
Poles
Connectivity
Tensor
Integrate
kernel
Imaging techniques
Line

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Identification of individuals with MCI via multimodality connectivity networks. / Wee, Chong Yaw; Yap, Pew Thian; Zhang, Daoqiang; Denny, Kevin; Wang, Lihong; Shen, Dinggang.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 6892 LNCS, No. PART 2, 11.10.2011, p. 277-284.

Research output: Contribution to journalArticle

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