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
. 2021 Feb 5:15:605115.
doi: 10.3389/fnins.2021.605115. eCollection 2021.

Diagnosis of Alzheimer's Disease Using Brain Network

Ramesh Kumar Lama  1 , Goo-Rak Kwon  1
Affiliations

Diagnosis of Alzheimer's Disease Using Brain Network

Ramesh Kumar Lama et al. Front Neurosci. .

Abstract

Recent studies suggest the brain functional connectivity impairment is the early event occurred in case of Alzheimer's disease (AD) as well as mild cognitive impairment (MCI). We model the brain as a graph based network to study these impairment. In this paper, we present a new diagnosis approach using graph theory based features from functional magnetic resonance (fMR) images to discriminate AD, MCI, and healthy control (HC) subjects using different classification techniques. These techniques include linear support vector machine (LSVM), and regularized extreme learning machine (RELM). We used pairwise Pearson's correlation-based functional connectivity to construct the brain network. We compare the classification performance of brain network using Alzheimer's disease neuroimaging initiative (ADNI) datasets. Node2vec graph embedding approach is employed to convert graph features to feature vectors. Experimental results show that the SVM with LASSO feature selection method generates better classification accuracy compared to other classification technique.

Keywords: Alzhieimer’s disease; brain network; extreme learning machine; node2vec; support vector machine.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Block diagram of the proposed diagnosis system.
FIGURE 2
FIGURE 2
Illustration of node selection in node2vec algorithm.
FIGURE 3
FIGURE 3
Average accuracy and standard deviation for AD against HC using RELM classification method on reduced datasets using LASSO feature selection.
FIGURE 4
FIGURE 4
The effect of different parameter values of Walk Length of Node2vec on performance (A) AD against HC, (B) HC against MCI, (C) AD against MCI.
See this image and copyright information in PMC

References

    1. Acosta-Cabronero J., Alley S., Williams G. B., Pengas G., Nestor P. J. (2012). Diffusion tensor metrics as biomarkers in Alzheimer’s disease. PLoS One 7:e49072. 10.1371/journal.pone.0049072 - DOI - PMC - PubMed
    1. Alzheimer’s association (2016). 2016 Alzheimer’s disease facts and figures. Alzheimers Dement. 12 459–509. 10.1016/j.jalz.2016.03.001 - DOI - PubMed
    1. American Psychiatric Association (1994). “Task force on DSM-IV,” in Diagnostic and Statistical Manual of Mental Disorders,” DSM-IV, 4th Edn, Vol. xxv (Washington, DC: American Psychiatric Association; ).
    1. Ashburner J. (2007). A fast diffeomorphic image registration algorithm. Neuroimage 38 95–113. 10.1016/j.neuroimage.2007.07.007 - DOI - PubMed
    1. Bajo R., Maestú F., Nevado A., Sancho M., Gutiérrez R., Campo P., et al. (2010). Functional connectivity in mild cognitive impairment during a memory task: implications for the disconnection hypothesis. J. Alzheimers Dis. 22 183–193. 10.3233/jad-2010-100177 - DOI - PubMed

LinkOut - more resources