Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Jan 27;12(1):e0170875.
doi: 10.1371/journal.pone.0170875. eCollection 2017.

Identification of Early-Stage Alzheimer's Disease Using Sulcal Morphology and Other Common Neuroimaging Indices

Kunpeng Cai  1   2 , Hong Xu  1 , Hao Guan  2   3 , Wanlin Zhu  2   4 , Jiyang Jiang  4   5 , Yue Cui  6   7 , Jicong Zhang  2   3 , Tao Liu  2   3   8 , Wei Wen  4   5
Affiliations

Identification of Early-Stage Alzheimer's Disease Using Sulcal Morphology and Other Common Neuroimaging Indices

Kunpeng Cai et al. PLoS One. .

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.

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Overview of the classification procedure using T1-weighted scans and the MMSE score [32].
Fig 2
Fig 2. Receiver operating characteristic (ROC) curves for using combined features including sulcal measures, cortical thickness, cortical volume and subcortical volume.
Fig 3
Fig 3. Receiver operating characteristic (ROC) curves for using combined features including sulcal measures, cortical thickness, cortical volume, subcortical volume and the MMSE score [32].
Fig 4
Fig 4. Receiver operating characteristic (ROC) curves for using combined features including cortical thickness, cortical volume, subcortical volume and the MMSE score [32].
Fig 5
Fig 5. Receiver operating characteristic (ROC) curves for using combined features including sulcal measures, subcortical volume and the MMSE score [32].
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Mucke L. Neuroscience: Alzheimer's disease. Nature. 2009;461(7266):895–7. 10.1038/461895a - DOI - PubMed
    1. Rogan S, Lippa CF. Alzheimer's disease and other dementias: a review. American journal of Alzheimer's disease and other dementias. 2002;17(1):11–7. - PMC - PubMed
    1. Yankner BA, Lu T, Loerch P. The aging brain. Annu Rev Pathol-Mech. 2008;3:41–66. - PubMed
    1. Prince M, Prina M, Guerchet M. Journey of Caring: an analysis of long-term care for Dementia. 2013.
    1. He Y, Wang L, Zang YF, Tian LX, Zhang XQ, Li KC, et al. Regional coherence changes in the early stages of Alzheimer's disease: A combined structural and resting-state functional MRI study. NeuroImage. 2007;35(2):488–500. 10.1016/j.neuroimage.2006.11.042 - DOI - PubMed

MeSH terms

Grants and funding

This research received support from the Natural Science Foundation of China (http://www.nsfc.gov.cn/, Grant No. 81401476 and Grant No. 61301005) and the National Key Research and Development Program of China (Grant No. 2016YFF0201002). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.