The connection network that construct based on averaged signals lacks a deeper interaction between the brain regions and the topological differences between patient group and normal control (NC) group. However, the averaged signal is not sufficient to reveal complex topological information of the brain region. They calculated the correlation of different regions based on these representative voxels. Simultaneously, for a large proportion of these methods, the first step was to segment the rs‐fMRI data into different regions by using some kind of partition template, and then signals of each region were averaged to generate a representative signal for each region.
These methods effectively used information of brain network topology, and they had obtained rational results at that time. In this model, they had adopted not only anatomical distance but also network topology, such as topology‐based link prediction methods and naïve Bayes classifiers (Si et al., 2019). Si had provided a brain network model for studying the mechanism underlying the development of AD and MCI. They drew a conclusion that patients with MCI and AD may experience disappearing some hub regions during disease progression (Ali et al., 2017). Ali Khazaee had used directed graph measures to identify alteration of brain network in MCI and AD. This model can explore an optimal set of predictors in AD abnormal brain (Cui et al., 2011). Cui and Liu had developed a Multivariate Predictors model, which extracted multiple features from different modalities of data. He defined connection as statistical associations in gray matter elevated mean diffusivity (MD) value between every two brain areas, and then, they constructed a symmetric connection matrix to analyze the AD and MCI abnormal brain function (Zhang et al., 2015). Zhang had constructed cortical diffusivity networks using graph theoretical approach. Liu had used a method, which is based on the partial correlations and indirect dependencies between each pair of brain regions to calculate the abnormal patterns of AD brain (Liu, Zhang, Yan, et al., 2012). So far, several groups have developed methods to investigate changes in functional brain organization in patients with AD and MCI.
Specially, it is more and more popular that using brain network and graph theory methods to analyze the AD and MCI abnormal brain function (Lei et al., 2020 Wang, Shen, et al., 2018). Recently, many studies revealed the differences of functional connectivity of AD brain regions based on resting‐state functional magnetic resonance imaging (rs‐fMRI) (Ali et al., 2017 Sharaev et al., 2016 Yu et al., 2020). Studies have shown that the brains of AD or MCI patients changed before the clinical symptoms appear early (Celone Willment et al., 2006 Greicius et al., 2004). Patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) show a decline in memory and cognitive functions than healthy people (Watt & Karl, 2017).