Random walks on B distributed resting-state functional connectivity to identify Alzheimer's disease and Mild Cognitive Impairment
Introduction
Alzheimer's disease (AD) is a continuum neurodegenerative disease that causes unchanging cognitive decline and neurodegenerative dysfunction. The presence of β-amyloid plaques and neurofibrillary tau deposits identifies AD (Dubois et al., 2016, Jack et al., 2018). It has been found that getting older promotes disease progression (Brookmeyer et al., 2007). Mild Cognitive Impairment (MCI) is an intermediary stage between healthy aging and dementia. Also, MCI patients turn into AD ones at the rate of 10–15% every year; it is a tremendous rate compared to the 1–2% risk of healthy aging transition to AD (Misra et al., 2009). There is no specific treatment for Alzheimer's disease. However, early detection of the disease helps plan for care and living arrangements, research new diagnostic methods, examine new medications, and experiment with strategies to prevent disease development (Paquerault, 2012).
Consequently, the diagnosis of the progression of AD and other stages of dementia are prominent. Researchers put much work into developing algorithms and methods to detect different stages of AD and dementia to tackle this requirement. This work is done using data from various neuroimaging methods, including Positron Emission Tomography (PET) (Duara et al., 2013), ElectroEncephaloGram (EEG) (Lehmann et al., 2007), Magnetoencephalography (MEG) (Engels et al., 2016), and functional Magnetic Resonance Imaging (fMRI) (Sheng et al., 2020).
Resting-state functional Magnetic Resonance Imaging (rs-fMRI) is a non-invasive neuroimaging technique with a high spatial and moderate temporal resolution. Accordingly, the blood-oxygenation level-dependent (BOLD) signal is used to measure spontaneous low-frequency fluctuations. Some studies have been leveraged rs-fMRI's BOLD signals to construct a functional connectivity network (FCN) of the brain. Then, the interactions between different brain regions are undertaken by FCN features to diagnose AD and MCI (Wang et al., 2018). Studies have employed various types of functional connectivity networks. However, most of the methods have used stationary FCNs. (Bi et al., 2018a, Bi et al., 2018b, Khazaee et al., 2015). However, some studies have also used dynamic FCNs (Jie et al., 2018). (Chen et al., 2016) proposed a High Order Resting-State Functional Connectivity method for detecting Early Mild Cognitive Impairment (EMCI) patients from healthy controls (HC). There have been several types of features extracted from FCNs for further use in classifiers. Some of these features include edge weights (Bi et al., 2018a, Zhang et al., 2015) and the graph metrics like Node strengths, Node degrees(Sheng et al., 2019), clustering coefficients (de Vos et al., 2018), betweenness centrality (Sheng et al., 2019), eigenvector centrality (Son et al., 2017), and Pagerank (Sheng et al., 2019). A few studies have leveraged graph embedding methods such as graph kernel (Sharaev et al., 2019). (Tang et al., 2019) introduced an algorithm based on random walks similar to node2vec to classify stages of MCI on brain networks derived from MRI images.
There exist several atlases for parcellated analysis of the brain. Automatic Anatomical Labeling (AAL) (Tzourio-Mazoyer et al., 2002), 264 putative functional areas (Power et al., 2011), Harvard-Oxford Cortical/Subcortical Atlas (Makris et al., 2006), and Yeo 2011 functional parcellations (Yeo et al., 2011Buckner et al., 2011) are some of the available atlases. Most studies use the AAL atlas to parcellate the brain into 90 or 116 regions (Bi et al., 2018b, Bi et al., 2018a, Jie et al., 2018, Khazaee et al., 2015). Parcellated analysis improves the signal-to-noise ratio (SNR) by averaging each region's time series (Glasser et al., 2016). Thus, more accurate parcellation leads to better results. (Khazaee et al., 2016) showed how three HC, MCI, and AD groups could be appropriately classified with an accuracy rate of 88.4% using 264 putative functional areas atlas.
Many studies have used brain parcellation based on one neurobiological property such as architecture, function, connectivity, or topography. HCP-MMP atlas (Glasser et al., 2016) combines multiple Neurobiological properties and delineates 180 areas per hemisphere from a group of 210 healthy young adults. The studies suggest that HCP-MMP is the most detailed cortical parcellation available in vivo2. (Sheng et al., 2019) applied this parcellation to get competitive results in AD, EMCI, and Late Mild Cognitive Impairment (LMCI) classification. The same parcellation was also used by (Sheng et al., 2020) to classify HC, AD, and MCI in another study. In some studies, the binary functional brain network was generated from the correlation matrix using an adaptive or static threshold. Other researchers have used the bare correlation matrix or the normalized correlation matrix (using Fisher r-to-z transformation) as their weighted FCNs.
This study proposes a method for automatically and efficiently classifying AD, EMCI, and LMCI patients using some new features that have not previously been explored. The details of the proposed method are shown in Fig. 1.
Section snippets
Subjects
“Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public–private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the
Results
The proposed method presents a trustworthy representation of the brain connectivity network, leading to promising results in the classification of Healthy Controls and individuals with EMCI, LMCI, and AD. Support vector machine and Logistic regression classifiers both provide similar results using the proposed mapping function. It provides ten percent improvement in accuracy and fifteen percent improvement in quadratic kappa scores for the ADNI database. For the synthetic database, these
Discussions
Experiments performed in this study for ADNI and synthetic databases show that using this mapping function can substantially improve performance. Nevertheless, Further research is needed to assess the efficacy of the proposed method with other neuroimaging modalities and neurological disorders in the future.
Conclusions
This study proposed a method for automatically and efficiently classifying AD, EMCI, and LMCI patients using some new features that have not previously been explored. This paper made the following main contributions:
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Node2vec (Grover and Leskovec, 2016), which has been proved to be one of the successful methods for node embeddings in many fields of research (Ata et al., 2018, Peng et al., 2019, Zhao et al., 2019), has been exploited as features for Support Vector Machine and Logistic Regression
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech;
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“Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf”