干旱区生态环境知识资源中心(Arid): Identifying discriminative features of brain network for prediction of Alzheimer's disease using graph theory and machine learning

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DOI	10.3389/fninf.2024.1384720
	Identifying discriminative features of brain network for prediction of Alzheimer's disease using graph theory and machine learning
	Karim, S. M. Shayez; Fahad, Md Shah; Rathore, R. S.
通讯作者	Rathore, RS
来源期刊	FRONTIERS IN NEUROINFORMATICS
EISSN	1662-5196
出版年	2024
卷号	18
英文摘要	Alzheimer's disease (AD) is a challenging neurodegenerative condition, necessitating early diagnosis and intervention. This research leverages machine learning (ML) and graph theory metrics, derived from resting-state functional magnetic resonance imaging (rs-fMRI) data to predict AD. Using Southwest University Adult Lifespan Dataset (SALD, age 21-76 years) and the Open Access Series of Imaging Studies (OASIS, age 64-95 years) dataset, containing 112 participants, various ML models were developed for the purpose of AD prediction. The study identifies key features for a comprehensive understanding of brain network topology and functional connectivity in AD. Through a 5-fold cross-validation, all models demonstrate substantial predictive capabilities (accuracy in 82-92% range), with the support vector machine model standing out as the best having an accuracy of 92%. Present study suggests that top 13 regions, identified based on most important discriminating features, have lost significant connections with thalamus. The functional connection strengths were consistently declined for substantia nigra, pars reticulata, substantia nigra, pars compacta, and nucleus accumbens among AD subjects as compared to healthy adults and aging individuals. The present finding corroborate with the earlier studies, employing various neuroimagining techniques. This research signifies the translational potential of a comprehensive approach integrating ML, graph theory and rs-fMRI analysis in AD prediction, offering potential biomarker for more accurate diagnostics and early prediction of AD.
英文关键词	machine learning Alzheimer's disease connectome neuronal connections brain regions fMRI graph theory network parameters
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001259440800001
WOS关键词	FUNCTIONAL CONNECTIVITY ; TOOLBOX ; NUCLEUS
WOS类目	Mathematical & Computational Biology ; Neurosciences
WOS研究方向	Mathematical & Computational Biology ; Neurosciences & Neurology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403856
推荐引用方式 GB/T 7714	Karim, S. M. Shayez,Fahad, Md Shah,Rathore, R. S.. Identifying discriminative features of brain network for prediction of Alzheimer's disease using graph theory and machine learning[J],2024,18.
APA	Karim, S. M. Shayez,Fahad, Md Shah,&Rathore, R. S..(2024).Identifying discriminative features of brain network for prediction of Alzheimer's disease using graph theory and machine learning.FRONTIERS IN NEUROINFORMATICS,18.
MLA	Karim, S. M. Shayez,et al."Identifying discriminative features of brain network for prediction of Alzheimer's disease using graph theory and machine learning".FRONTIERS IN NEUROINFORMATICS 18(2024).