多发性硬化网络节点改变的基于体素度中心度的fMRI研究

doi:10.3969/j.issn.2095-9400.2019.11.007

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Abstract Objective: To explore the abnormal alteration of intrinsic functional hubs and their relationship with the clinical features in relapsing-remitting multiple sclerosis (RRMS) using degree centrality approach. Methods: A total of 26 RRMS and 27 status-matched healthy groups were recruited. RRMS patients underwent a physical examination using Expanded Disability Status Scale (EDSS) by an experienced neurologist. Degree centrality and functional connectivity methods were used to evaluate the features of abnormal intrinsic functional hubs. Pearson correlation analysis was conducted to investigate the relationship between clinical features and abnormal functional hubs. Receiver operating characteristic (ROC) curve was applied to calculate the sensitivity and specificity of those altered functional hubs in distinguishing the RRMS from the HGs. Results: The covered brain areas of degree centrality differences didn’t change with different thresholds of correlation coefficient r values. Compared with the HGs, the RRMS demonstrated significantly higher degree centrality values in multiple brain areas and lower degree centrality values in bilateral salience network and left cingulate gyrus. Decreased functional connectivity was found within the left-right salience network, and between the salience network and the cingulate gyrus. Disease duration of RRMS revealed a negative linear correlation with the degree centrality value in the left temporal pole (r=-0.483, P=0.020). EDSS revealed a positive linear correlation with the degree centrality value in the right superior parietal lobule (r=0.485, P=0.019), and a negative linear correlation with the degree centrality value in the right temporal pole (r=-0.430, P=0.041). ROC analysis showed good performance of these abnormal hubs in distinguishing the RRMS from the HGs (AUC=0.943, sensitivity was 96.3%, specificity was 88.5%). Conclusion: The functional deficits in the cingulate gyrus and temporal pole in RRMS could predict disease progression and disability status, which may expand our understanding of functional characteristics and provide a new insight into the pathophysiological mechanism of RRMS.

Key words： multiple sclerosis degree centrality functional connectivity functional magnetic resonance imaging

Received: 11 March 2019

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	LIU Chang’en
	Du Xiaofeng

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https://xb.wmu.edu.cn/EN/ 10.3969/j.issn.2095-9400.2019.11.007 OR https://xb.wmu.edu.cn/EN/Y2019/V49/I11/819

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