Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks. Issue 125 (March 2016)

• Record Type: Journal Article
• Authors: Lin, Lan; Jin, Cong; Fu, Zhenrong; Zhang, Baiwen; Bin, Guangyu; Wu, Shuicai
• Is Part Of: Computer methods and programs in biomedicine. Issue 125(2016)
• Page Start: 8
• Page End: 17
• Publication Date: 2016-03
• Subjects: MRI -- Healthy brain ageing -- Connectome -- DTI -- White matter; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2015.11.012
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We proposed a novel computational approach for modeling the normal elderly subjects's brain age by connectivity analyses of networks of the brain. Principal component analysis (PCA) is applied to reduce the redundancy in network topological parameters. BP artificial neural network (BPANN) is improved by hybrid genetic algorithm (GA) and Levenberg–Marquardt (LM) algorithm to model the relation among principal components (PCs) and brain age. The method has shown good performance for old cohort with limited samples. Abstract: Brain ageing is followed by changes of the connectivity of white matter (WM) and changes of the grey matter (GM) concentration. Neurodegenerative disease is more vulnerable to an accelerated brain ageing, which is associated with prospective cognitive decline and disease severity. Accurate detection of accelerated ageing based on brain network analysis has a great potential for early interventions designed to hinder atypical brain changes. To capture the brain ageing, we proposed a novel computational approach for modeling the 112 normal older subjects (aged 50–79 years) brain age by connectivity analyses of networks of the brain. Our proposed method applied principal component analysis (PCA) to reduce the redundancy in network topological parameters. Back propagation artificial neural network (BPANN) improved by hybrid genetic algorithm (GA) and Levenberg–Marquardt (LM) algorithm is established to model the relation among principal components (PCs) and brain age. The predicted brain age is strongly correlated with chronological age ( r = 0.8). The model has mean absolute error (MAE) of 4.29 years. Therefore, we believe the method can provide a possible way to quantitatively describe the typical and atypical network organization of human brain and serve as a biomarker for presymptomatic detection of neurodegenerative diseases in the future.