Identification of MCI Individuals Using Structural and Functional Connectivity Networks

By Chong-Yaw Wee, Pew-Thian Yap, Daoqiang Zhang, Kevin Denny, Jeffrey N. Browndyke, Guy G. Potter, Kathleen Welsh-Bohmer, Lihong Wang, Dinggang Shen. NeuroImage, 2011

384 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Psychiatry and Mental health, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a9fb8c99e79fe32293a82ec986ca9941de4c7d53

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In Cognitive Neuroscience

In Psychiatry and Mental health

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Single Subject Prediction of Brain Disorders in Neuroimaging: Promises and Pitfalls (2016, positive): “3%) compared to using each modality independently (Wee et al., 2012).”
The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features (2018, neutral): “, rsFCS and local clustering coefficient) (Wee et al., 2012) or data-driven (e.”
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis (2014, positive): “…modalities helps enhance the diagnostic accuracy (Cui et al., 2011; Fan et al., 2007a; Hinrichs et al., 2011; Kohannim et al., 2010; Perrin et al., 2009; Suk and Shen, 2013;Walhovd et al., 2010;Wee et al., 2012; Westman et al., 2012; Yuan et al., 2012; Zhang and Shen, 2012; Zhang et al., 2011).”
Latent feature representation with stacked auto-encoder for AD/MCI diagnosis (2013, methodology): “…data for discriminating AD/MCI patients from healthy normal controls (HC) (Fan et al. 2007; Perrin et al. 2009; Kohannim et al. 2010; Walhovd et al. 2010; Cui et al. 2011; Hinrichs et al. 2011; Zhang et al. 2011; Wee et al. 2012; Westman et al. 2012; Yuan et al. 2012; Zhang and Shen 2012).”