Functional connectivity between white matter and gray matter based on fMRI for Alzheimer's disease classification

Abstract

Introduction Alzheimer's disease (AD) is a chronic neurodegenerative disease that generally starts slowly and leads to deterioration over time. Finding biomarkers more effective to predict AD transition is important for clinical medicine. And current research indicated that the lesion regions occur in both gray matter (GM) and white matter (WM). Methods This paper extracted BOLD time series from WM and GM, combined WM and GM together for analysis, constructed functional connectivity (FC) of static (sWGFC) and dynamic (dWGFC) between WM and GM, as well as static (sGFC) and dynamic (dGFC) FC within GM in order to evaluate the methods and areas most useful as feature sets for distinguishing NC from AD. These features will be evaluated using support vector machine (SVM) classifiers. Results The FC constructed by WM BOLD time series based on fMRI showed widely differences between the AD group and NC group. In terms of the results of the classification, the performance of feature subsets selected from sWGFC was better than sGFC, and the performance of feature subsets selected from dWGFC was better than dGFC. Overall, the feature subsets selected from dWGFC was the best. Conclusion These results indicated that there is a wide range of disconnection between WM and GM in AD, and association between WM and GM based on fMRI only is an effective strategy, and the FC between WM and GM could be a potential biomarker in the process of cognitive impairment and AD.

Diagnostic power of resting‐state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review

“…As a result, 36 articles met our inclusion criteria (13 AD, 14 MCI, and 9 combined AD/MCI articles) and were included in the quantitative synthesis. Thus, the total number of the final included articles was 36 (Balthazar, de Campos, Franco, Damasceno, & Cendes, 2014; Bi, Shu, Sun, & Xu, 2018; Challis et al, 2015; X. Chen et al, 2017; Dai et al, 2012; de Marco et al, 2017; de Vos et al, 2018; Dyrba et al, 2015; Hojjati et al, 2019; Hojjati, Ebrahimzadeh, Khazaee, & Babajani‐Feremi, 2017, 2018; X. Jiang, Zhang, & Zhu, 2014; Jin et al, 2020; Khazaee, Ebrahimzadeh, & Babajani‐Feremi, 2017; Kilian, Bröckel, Overmeyer, Dieterich, & Endrass, 2020; Koch et al, 2012; J. Lee et al, 2015; Y. Li, Wee, Jie, Peng, & Shen, 2014; Lisowska & Rekik, 2019; J. Liu, Pan, Wu, & Wang, 2020; Miao, Wu, Li, Chen, & Yao, 2011; Park et al, 2017; Qian, Zheng, Shang, Zhang, & Zhang, 2018; Qureshi et al, 2019; Schouten et al, 2016; Son, Kim, & Park, 2017; Suk, Wee, Lee, & Shen, 2015; Teipel et al, 2016; Teipel et al, 2017; Wee et al, 2012; Yokoi et al, 2018; Yu et al, 2017; L. Zhang et al, 2020; Y. Zhang, Zhang, Chen, Lee, & Shen, 2017; J. Zhao, Ding, Du, Wang, & Men, 2019; Zheng et al, 2019; Zhu et al, 2014) Overall, the articles had low risk of bias.…”

Abnormal Functional Connectivity of the Amygdala in Mild Cognitive Impairment Patients With Depression Symptoms Revealed by Resting-State fMRI

“…The training parameters of the SVM model were the default parameters provided by MATLAB 2014a. We also computed the following corresponding evaluation indices: accuracy (ACC), sensitivity (SEN), specificity (SPE), area under the receiver operating characteristic curve (AUC), and F-score (33).…”

Brain simulation augments machine-learning-based classification of dementia

“…SVM is a widely used method for supervised ML classification problems and has been well established in recent Neuroscience literature (Rathore, Habes et al 2017, Rondina, Ferreira et al 2018, Shaikh and Ali 2019, Zhao, Ding et al 2019. SVMs have been extensively employed due to their robustness, simplicity to implement, and because they can also be employed as non-linear classifiers by making simple variations.…”