Point process analysis in brain networks of patients with diabetes

Noise and individual differences arise from disturbances in the effective use of resting-state functional magnetic resonance image (fMRI) datasets. In this study, the point process is used to treat fMRI datasets of healthy controls and patients with diabetes; then, functional brain networks of subjects are established using two sets of BOLD signals. The results illustrate that differences between the healthy controls and the patients were more obvious in point process signals than nonpoint process signals. Our results also suggest that there is a higher recognition accuracy of the signals by preprocessing with the point process. These findings may suggest that the point process approach can reduce BOLD signals noise, providing a new method for functional magnetic resonance data preprocessing, and may provide a promising method for early data preprocessing in computer-aided disease diagnostics.