Research Article: Identification of Early-Stage Alzheimer’s Disease Using Sulcal Morphology and Other Common Neuroimaging Indices

Date Published: January 27, 2017

Publisher: Public Library of Science

Author(s): Kunpeng Cai, Hong Xu, Hao Guan, Wanlin Zhu, Jiyang Jiang, Yue Cui, Jicong Zhang, Tao Liu, Wei Wen, Stephen D. Ginsberg.

http://doi.org/10.1371/journal.pone.0170875

Abstract

Identifying Alzheimer’s disease (AD) at its early stage is of major interest in AD research. Previous studies have suggested that abnormalities in regional sulcal width and global sulcal index (g-SI) are characteristics of patients with early-stage AD. In this study, we investigated sulcal width and three other common neuroimaging morphological measures (cortical thickness, cortical volume, and subcortical volume) to identify early-stage AD. These measures were evaluated in 150 participants, including 75 normal controls (NC) and 75 patients with early-stage AD. The global sulcal index (g-SI) and the width of five individual sulci (the superior frontal, intra-parietal, superior temporal, central, and Sylvian fissure) were extracted from 3D T1-weighted images. The discriminative performances of the other three traditional neuroimaging morphological measures were also examined. Information Gain (IG) was used to select a subset of features to provide significant information for separating NC and early-stage AD subjects. Based on the four modalities of the individual measures, i.e., sulcal measures, cortical thickness, cortical volume, subcortical volume, and combinations of these individual measures, three types of classifiers (Naïve Bayes, Logistic Regression and Support Vector Machine) were applied to compare the classification performances. We observed that sulcal measures were either superior than or equal to the other measures used for classification. Specifically, the g-SI and the width of the Sylvian fissure were two of the most sensitive sulcal measures and could be useful neuroanatomical markers for detecting early-stage AD. There were no significant differences between the three classifiers that we tested when using the same neuroanatomical features.

Partial Text

Alzheimer’s disease (AD) is the most common cause of dementia, with typical characteristics of progressive cognitive decline such as memory impairment and the degeneration of reasoning ability [1,2]. The onset of AD is insidious, and the decline in cognition may not manifest until effective interventions become difficult [1,3]. A previous study showed that facilitating intervention at an early stage could effectively alleviate the symptoms of the disease [4]. Therefore, early diagnosis of AD will benefit patients, families and society as a whole.

For all three classifiers, performance characteristics were determined as the average of cross-validation experiments for each of the 8 approaches to classification: sulcal measures (SM) alone, cortical thickness (CT) alone, cortical volume (CV) alone, subcortical volume (SV) alone (Table 2), a combination of all of these measures (Table 3), a combination of all of these measures plus the MMSE scores (Table 4), and a combination of CT, CV, SV, and MMSE (Table 5), and, finally, a combination of SM, SV, and MMSE (Table 6).

The present study investigated the use of sulcal measures in the diagnosis of early-stage AD, and we evaluated the performance of three different classification methods. In addition, we selected the most discriminative features that would most contribute to the diagnosis of early-stage AD by using IG algorithm.

 

Source:

http://doi.org/10.1371/journal.pone.0170875

 

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