Abnormal brain functional networks in systemic lupus erythematosus: a graph theory, network-based statistic and machine learning study.

Systemic lupus erythematosus patients' brain functional network impairments are incompletely clarified. This study investigates the brain functional network topological alterations in systemic lupus erythematosus and the application of machine learning to the classification of systemic lupus erythematosus and healthy controls. Resting-state functional MRI data from 127 systemic lupus erythematosus patients and 102 healthy controls were used. The pre-processing process involved using automated anatomical labelling atlas to compute time series data for 116 brain regions. A functional connectivity network was then created by assessing the Pearson correlation between the time series of these brain regions. The GRETNA toolbox was used to compute the difference in topological attributes between groups. Variations in regional networks among groups were evaluated using non-parametric permutation tests that rely on network-based statistical analysis. With the functional connectivity network metrics as features and network-based statistical analysis as the feature selection method, network-based statistical analysis Predict software was used to classify systemic lupus erythematosus from controls by support vector machine. The subnets that contributed the most to systemic lupus erythematosus classification were also identified. For global indicators, the systemic lupus erythematosus group exhibited significantly lower values for the normalized clustering coefficient (