Identification of MCI individuals using structural and functional connectivity networks.

Published

Journal Article

Different imaging modalities provide essential complementary information that can be used to enhance our understanding of brain disorders. This study focuses on integrating multiple imaging modalities to identify individuals at risk for mild cognitive impairment (MCI). MCI, often an early stage of Alzheimer's disease (AD), is difficult to diagnose due to its very mild or insignificant symptoms of cognitive impairment. Recent emergence of brain network analysis has made characterization of neurological disorders at a whole-brain connectivity level possible, thus providing new avenues for brain diseases classification. Employing multiple-kernel Support Vector Machines (SVMs), we attempt to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance. Our results indicate that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently. The classification accuracy obtained by the proposed method is 96.3%, which is an increase of at least 7.4% from the single modality-based methods and the direct data fusion method. A cross-validation estimation of the generalization performance gives an area of 0.953 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. The multimodality classification approach hence allows more accurate early detection of brain abnormalities with greater sensitivity.

Full Text

Duke Authors

Cited Authors

  • Wee, C-Y; Yap, P-T; Zhang, D; Denny, K; Browndyke, JN; Potter, GG; Welsh-Bohmer, KA; Wang, L; Shen, D

Published Date

  • February 2012

Published In

Volume / Issue

  • 59 / 3

Start / End Page

  • 2045 - 2056

PubMed ID

  • 22019883

Pubmed Central ID

  • 22019883

Electronic International Standard Serial Number (EISSN)

  • 1095-9572

International Standard Serial Number (ISSN)

  • 1053-8119

Digital Object Identifier (DOI)

  • 10.1016/j.neuroimage.2011.10.015

Language

  • eng