Network‐based analysis and individualized prediction
REGRESSION
From the perspective of whole‐brain functional connectivity, we used regression analysis to explore the functional connectivity mechanism of rTMS treatment effects. First, we constructed whole‐brain functional networks before and after rTMS treatment (Figure Results of network‐based and CPM analysis. (A) The spatial extent of both positive and negative networks correlated with Δ memory function included 218 edges (144 positive, 74 negative). The positive network included relatively more connections between DMN and DAN, and the negative network included relatively more within‐network connections for DMN and LN. The network‐based connectivity at baseline could predict Δ memory function after rTMS treatment (positive network: Next, the CPM method was used to predict rTMS treatment effects based on network‐based connectivity obtained from above analyses at baseline. The network‐based connectivity at baseline could predict Δ memory function after rTMS treatment (positive network: 