Brain network analyses of diffusion tensor imaging for brain aging

Song Xu; Xufeng Yao; Liting Han; Yuting Lv; Xixi Bu; Gan Huang; Yifeng Fan; Tonggang Yu; Gang Huang; Song Xu; Xufeng Yao; Liting Han; Yuting Lv; Xixi Bu; Gan Huang; Yifeng Fan; Tonggang Yu; Gang Huang

doi:10.3934/mbe.2021303

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 5: 6066-6078. doi: 10.3934/mbe.2021303

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Brain network analyses of diffusion tensor imaging for brain aging

1.
College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
2.
Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
3.
School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
4.
School of Medical Imaging, Hangzhou Medical College, Hangzhou 310053, China
5.
Shanghai Gamma Knife Hospital, Fudan University, Shanghai 200235, China

Academic Editor:Carlo Ricciardi

Received: 26 May 2021 Accepted: 05 July 2021 Published: 07 July 2021

The approach of graph-based diffusion tensor imaging (DTI) networks has been used to explore the complicated structural connectivity of brain aging. In this study, the changes of DTI networks of brain aging were quantitatively and qualitatively investigated by comparing the characteristics of brain network. A cohort of 60 volunteers was enrolled and equally divided into young adults (YA) and older adults (OA) groups. The network characteristics of critical nodes, path length (L_p), clustering coefficient (C_p), global efficiency (E_global), local efficiency (E_local), strength (S_p), and small world attribute (σ) were employed to evaluate the DTI networks at the levels of whole brain, bilateral hemispheres and critical brain regions. The correlations between each network characteristic and age were predicted, respectively. Our findings suggested that the DTI networks produced significant changes in network configurations at the critical nodes and node edges for the YA and OA groups. The analysis of whole brains network revealed that L_p, C_p increased (p < 0.05, positive correlation), E_global, E_local, S_p decreased (p < 0.05, negative correlation), and σ unchanged (p ≥ 0.05, non-correlation) between the YA and OA groups. The analyses of bilateral hemispheres and brain regions showed similar results as that of the whole-brain analysis. Therefore the proposed scheme of DTI networks could be used to evaluate the WM changes of brain aging, and the network characteristics of critical nodes exhibited valuable indications for WM degeneration.
- brain aging,
- diffusion tensor imaging (DTI),
- network characteristics,
- white matter (WM),
- critical nodes
Citation: Song Xu, Xufeng Yao, Liting Han, Yuting Lv, Xixi Bu, Gan Huang, Yifeng Fan, Tonggang Yu, Gang Huang. Brain network analyses of diffusion tensor imaging for brain aging[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 6066-6078. doi: 10.3934/mbe.2021303

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Abstract

The approach of graph-based diffusion tensor imaging (DTI) networks has been used to explore the complicated structural connectivity of brain aging. In this study, the changes of DTI networks of brain aging were quantitatively and qualitatively investigated by comparing the characteristics of brain network. A cohort of 60 volunteers was enrolled and equally divided into young adults (YA) and older adults (OA) groups. The network characteristics of critical nodes, path length (L_p), clustering coefficient (C_p), global efficiency (E_global), local efficiency (E_local), strength (S_p), and small world attribute (σ) were employed to evaluate the DTI networks at the levels of whole brain, bilateral hemispheres and critical brain regions. The correlations between each network characteristic and age were predicted, respectively. Our findings suggested that the DTI networks produced significant changes in network configurations at the critical nodes and node edges for the YA and OA groups. The analysis of whole brains network revealed that L_p, C_p increased (p < 0.05, positive correlation), E_global, E_local, S_p decreased (p < 0.05, negative correlation), and σ unchanged (p ≥ 0.05, non-correlation) between the YA and OA groups. The analyses of bilateral hemispheres and brain regions showed similar results as that of the whole-brain analysis. Therefore the proposed scheme of DTI networks could be used to evaluate the WM changes of brain aging, and the network characteristics of critical nodes exhibited valuable indications for WM degeneration.

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