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Wen C, Zeng Q, Zhou R, Xie L, Yu J, Zhang C, Wang J, Yu Y, Gu Y, Cao G, Feng Y, Wang M. Characterization of local white matter microstructural alterations in Alzheimer's disease: A reproducible study. Comput Biol Med 2024; 179:108750. [PMID: 38996551 DOI: 10.1016/j.compbiomed.2024.108750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 05/24/2024] [Accepted: 06/08/2024] [Indexed: 07/14/2024]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease with a close association with microstructural alterations in white matter (WM). Current studies lack the characterization and further validation of specific regions in WM fiber tracts in AD. This study subdivided fiber tracts into multiple fiber clusters on the basis of automated fiber clustering and performed quantitative analysis along the fiber clusters to identify local WM microstructural alterations in AD. Diffusion tensor imaging data from a public dataset (53 patients with AD and 70 healthy controls [HCs]) and a clinical dataset (27 patients with AD and 19 HCs) were included for mutual validation. Whole-brain tractograms were automatically subdivided into 800 clusters through the automatic fiber clustering approach. Then, 100 segments were divided along the clusters, and the diffusion properties of each segment were calculated. Results showed that patients with AD had significantly lower fraction anisotropy (FA) and significantly higher mean diffusivity (MD) in some regions of the fiber clusters in the cingulum bundle, uncinate fasciculus, external capsule, and corpus callosum than HCs. Importantly, these changes were reproducible across the two datasets. Correlation analysis revealed a positive correlation between FA and Mini-Mental State Examination (MMSE) scores and a negative correlation between MD and MMSE in these clusters. The accuracy of the constructed classifier reached 89.76% with an area under the curve of 0.93. This finding indicates that this study can effectively identify local WM microstructural changes in AD and provides new insight into the analysis and diagnosis of WM abnormalities in patients with AD.
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Affiliation(s)
- Caiyun Wen
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Qingrun Zeng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Ronghui Zhou
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Lei Xie
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jiangli Yu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Chengzhe Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jingqiang Wang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Yan Yu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Yixin Gu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Guoquan Cao
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Yuanjing Feng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Meihao Wang
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
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Wang J, Liu X, Wang X, Hu Y, Zeng Q, Lin Z, Xiong N, Feng Y. Alterations of white matter tracts and topological properties of structural networks in hemifacial spasm. NMR IN BIOMEDICINE 2022; 35:e4756. [PMID: 35488376 DOI: 10.1002/nbm.4756] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 03/31/2022] [Accepted: 04/28/2022] [Indexed: 06/14/2023]
Abstract
Hemifacial spasm (HFS) is characterized by involuntary and paroxysmal muscle contractions on the hemiface. It is generally believed that HFS is caused by neurovascular compression at the root exit zone of the facial nerve. In recent years, the structural alterations of brains with HFS have aroused growing concern. However, little attention has been directed towards the possible involvement of specific white matter (WM) tracts and the topological properties of structural networks in HFS. In the present study, diffusion magnetic resonance imaging tractography was utilized to construct structural networks and perform tractometric analysis. The diffusion tensor imaging scalar parameters along with the WM tracts, and the topological parameters of global networks and subnetworks, were assessed in 62 HFS patients and 57 demographically matched healthy controls (HCs). Moreover, we investigated the correlation of these parameters with disease-clinical-level (DCL) and disease-duration-time (DDT) of HFS patients. Compared with HCs, HFS patients had additional hub regions including the amygdala, ventromedial putamen, lateral occipital cortex, and rostral cuneus gyrus. Furthermore, HFS patients showed significant alternations with specific topological properties in some structural subnetworks, including the limbic, default mode, dorsal attention, somato-motor, and control networks, as well as diffusion properties in some WM tracts, including the superior longitudinal fasciculus, cingulum bundle, thalamo-frontal, and corpus callosum. These subnetworks and tracts were associated with the regulation of emotion, motor function, vision, and attention. Notably, we also found that the parameters with subnetworks and tracts exhibited correlations with DCL and DDT. In addition to corroborating previous findings in HFS, this study demonstrates the changed microstructures in specific locations along with the fiber tracts and changed topological properties in structural subnetworks.
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Affiliation(s)
- Jingqiang Wang
- Institution of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Xiaoming Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, Hubei, China
| | - Xinyi Wang
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yuhuan Hu
- Institution of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Qingrun Zeng
- Institution of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Zhicheng Lin
- Mclean Hospital, Harvard Medical School, Belmont, Massachusetts, USA
| | - Nian Xiong
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yuanjing Feng
- Institution of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
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Xu C, Sun G, Liang R, Xu X. Vector Field Streamline Clustering Framework for Brain Fiber Tract Segmentation. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2021.3094555] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Wu H, Yan H, Yang Y, Xu M, Shi Y, Zeng W, Li J, Zhang J, Chang C, Wang N. Occupational Neuroplasticity in the Human Brain: A Critical Review and Meta-Analysis of Neuroimaging Studies. Front Hum Neurosci 2020; 14:215. [PMID: 32760257 PMCID: PMC7373999 DOI: 10.3389/fnhum.2020.00215] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Accepted: 05/13/2020] [Indexed: 12/14/2022] Open
Abstract
Many studies have revealed the structural or functional brain changes induced by occupational factors. However, it remains largely unknown how occupation-related connectivity shapes the brain. In this paper, we denote occupational neuroplasticity as the neuroplasticity that takes place to satisfy the occupational requirements by extensively professional training and to accommodate the long-term, professional work of daily life, and a critical review of occupational neuroplasticity related to the changes in brain structure and functional networks has been primarily presented. Furthermore, meta-analysis revealed a neurophysiological mechanism of occupational neuroplasticity caused by professional experience. This meta-analysis of functional neuroimaging studies showed that experts displayed stronger activation in the left precentral gyrus [Brodmann area (BA)6], left middle frontal gyrus (BA6), and right inferior frontal gyrus (BA9) than novices, while meta-analysis of structural studies suggested that experts had a greater gray matter volume in the bilateral superior temporal gyrus (BA22) and right putamen than novices. Together, these findings not only expand the current understanding of the common neurophysiological basis of occupational neuroplasticity across different occupations and highlight some possible targets for neural modulation of occupational neuroplasticity but also provide a new perspective for occupational science research.
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Affiliation(s)
- Huijun Wu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Hongjie Yan
- Department of Neurology, Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China
| | - Yang Yang
- Center for Brain Science and Learning Difficulties, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Min Xu
- Center for Brain Disorders and Cognitive Science, Shenzhen University, Shenzhen, China
| | - Yuhu Shi
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China
| | - Weiming Zeng
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China
| | - Jiewei Li
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Jian Zhang
- School of Pharmacy, Health Science Center, Shenzhen University, Shenzhen, China
| | - Chunqi Chang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Pengcheng Laboratory, Shenzhen, China
| | - Nizhuan Wang
- Artificial Intelligence & Neuro-Informatics Engineering (ARINE) Laboratory, School of Computer Engineering, Jiangsu Ocean University, Lianyungang, China
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