Objective To explore the characteristics of the default memory network (DMN) and working memory network (WMN) at resting state brain functional network of exercise addiction people. Methods Twenty-nine sports addicts and 26 non-sports addicts matched by sex, age, average education level and sports dependence were screened by the exercise addiction index (EAI). Resting status brain scanning was performed with 3.0T magnetic resonance scanner.Sparse approximation coefficients independent component analysis (SACICA) model was used to analyze the independent components of brain networks. Results Compared with the DMN template, four features were extracted, including basic conformity , less frontal lobe , more frontal lobe and less occipitoparietal lobe . Compared with the parameters of basic conformity , the proportion of exercise addiction group (33.3%, 9/27) was higher than that of control group (18.2%, 4/22). In the other three parameters, the proportion of exercise addiction group (37.0%, 10/27; 3.7%, 1/27; 22.2%, 6/27) was lower than those of control group (45.5%, 10/22; 22.7%, 5/22; 27.3%, 6/22). But Chi-square test showed that there was no significant difference between the two groups(all P>0.05). Compared with the WMN template, six features were extracted, including basic conformity , more frontal and parietal lobes , more parietal lobes , more frontal lobes , less frontal lobes and less parietal lobes . The percentages of the first three features in exercise addiction group (22.2%, 6/27; 7.4%, 2/27; 7.4%, 2/27) were less than those in the control group (45.5%, 10/22; 22.7%, 5/22; 9.1%, 2/22), while the percentages of the last three features in the exercise addiction group (7.4%, 2/27; 37.0%, 10/27; 14.8%, 4/27) were higher than those in the control group (4.5%, 1/22; 13.6%, 3/22; 0, 0). Chi-square test showed that there was no significant difference in all features between the two groups was statistically(P>0.05). Conclusion No significant characteristic changes are found in DMN and WMN networks of exercise addiction population.
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Ding et al. (2019) studied this question.
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