Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis



Li, Wenbin, Wei, Qianqian, Hou, Yanbing, Lei, Du, Ai, Yuan, Qin, Kun, Yang, Jing, Kemp, Graham J ORCID: 0000-0002-8324-9666, Shang, Huifang and Gong, Qiyong
(2021) Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis. TRANSLATIONAL NEURODEGENERATION, 10 (1). 35-.

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Abstract

<h4>Objective</h4>There is increasing evidence that amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease impacting large-scale brain networks. However, it is still unclear which structural networks are associated with the disease and whether the network connectomics are associated with disease progression. This study was aimed to characterize the network abnormalities in ALS and to identify the network-based biomarkers that predict the ALS baseline progression rate.<h4>Methods</h4>Magnetic resonance imaging was performed on 73 patients with sporadic ALS and 100 healthy participants to acquire diffusion-weighted magnetic resonance images and construct white matter (WM) networks using tractography methods. The global and regional network properties were compared between ALS and healthy subjects. The single-subject WM network matrices of patients were used to predict the ALS baseline progression rate using machine learning algorithms.<h4>Results</h4>Compared with the healthy participants, the patients with ALS showed significantly decreased clustering coefficient C<sub>p</sub> (P = 0.0034, t = 2.98), normalized clustering coefficient γ (P = 0.039, t = 2.08), and small-worldness σ (P = 0.038, t = 2.10) at the global network level. The patients also showed decreased regional centralities in motor and non-motor systems including the frontal, temporal and subcortical regions. Using the single-subject structural connection matrix, our classification model could distinguish patients with fast versus slow progression rate with an average accuracy of 85%.<h4>Conclusion</h4>Disruption of the WM structural networks in ALS is indicated by weaker small-worldness and disturbances in regions outside of the motor systems, extending the classical pathophysiological understanding of ALS as a motor disorder. The individual WM structural network matrices of ALS patients are potential neuroimaging biomarkers for the baseline disease progression in clinical practice.

Item Type: Article
Uncontrolled Keywords: Amyotrophic lateral sclerosis, White matter, DTI, Network, Connectomics, Machine learning, Psychoradiology
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User: Symplectic Admin
Date Deposited: 17 Nov 2021 09:10
Last Modified: 18 Jan 2023 21:24
DOI: 10.1186/s40035-021-00255-0
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3143313