Graph Theory-Based Brain Network Connectivity Analysis and Classification of Alzheimer's Disease

被引:1
|
作者
Thushara, A. [1 ]
Amma, C. Ushadevi [2 ]
John, Ansamma [1 ]
机构
[1] APJ Abdul Kalam Technol Univ, TKM Coll Engn, Dept Comp Sci & Engn, Kollam 691005, Kerala, India
[2] Amrita Vishwa Vidyapeetham, Dept Elect & Commun Engn, Amritapuri Campus, Amritapuri 690525, Kerala, India
关键词
Alzheimer's disease; diffusion-weighted imaging; graph theory; machine learning; DIFFUSION; PARCELLATION; TRACTOGRAPHY; CORTEX;
D O I
10.1142/S021946782240006X
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Alzheimer's Disease (AD) is basically a progressive neurodegenerative disorder associated with abnormal brain networks that affect millions of elderly people and degrades their quality of life. The abnormalities in brain networks are due to the disruption of White Matter (WM) fiber tracts that connect the brain regions. Diffusion-Weighted Imaging (DWI) captures the brain's WM integrity. Here, the correlation betwixt the WM degeneration and also AD is investigated by utilizing graph theory as well as Machine Learning (ML) algorithms. By using the DW image obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI) database, the brain graph of each subject is constructed. The features extracted from the brain graph form the basis to differentiate between Mild Cognitive Impairment (MCI), Control Normal (CN) and AD subjects. Performance evaluation is done using binary and multiclass classification algorithms and obtained an accuracy that outperforms the current top-notch DWI-based studies.
引用
收藏
页数:19
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