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Then the networks were discriminated with K-means-clustering-algorithm into different groups and followed by comparative analysis to the groups and HC. Regression analysis was used to investigate the brain regions significantly contributed to relapse.<\/jats:p><\/jats:sec><jats:sec id=\"S0033291721003937_sec_a3\" sec-type=\"results\"><jats:title>Results<\/jats:title><jats:p>Sixty MMT patients were classified into two groups according to their brain-network patterns calculated by the best clustering-number-K. The two groups had no difference in the demographic, psychological indicators and clinical information except relapse rate and total heroin consumption. The group with high-relapse had a wider range of DC changes in the cortical\u2212striatal\u2212thalamic circuit relative to HC and a reduced DC in the mesocorticolimbic circuit relative to the low-relapse group. DC activity in NAc, vACC, hippocampus and amygdala were closely related with relapse.<\/jats:p><\/jats:sec><jats:sec id=\"S0033291721003937_sec_a4\" sec-type=\"conclusions\"><jats:title>Conclusion<\/jats:title><jats:p>MMT patients can be identified and classified into two subgroups with significantly different relapse rates by defining distinct brain-network patterns even if we are blind to their relapse outcomes in advance. This may provide a new strategy to optimize MMT.<\/jats:p><\/jats:sec>","DOI":"10.1017\/s0033291721003937","type":"journal-article","created":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T07:29:48Z","timestamp":1635319788000},"page":"2216-2228","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":7,"title":["Relapse risk revealed by degree centrality and cluster analysis in heroin addicts undergoing methadone maintenance 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