Baseline functional connectivity predicts rTMS treatment response
AD
It is critically important to be capable of predicting the possible response to rTMS stimulation using rs‐fMRI techniques. We utilized baseline connectivity as a predictive network according to the results of previous seed‐based and network‐based analyses. Our findings indicated that the connectivity patterns of DMN subsystems or large‐scale networks at baseline significantly predicted the therapeutic effect of rTMS. Among these, the connectivity patterns of large‐scale networks at baseline showed the best ability to predict the improvement of language function after rTMS treatment. Importantly, this good predictive capacity may further provide converging evidence of distinct modulation of DMN subsystems and large‐scale networks in response to rTMS intervention. A recent study focusing on rTMS‐induced connectivity within the DMN in AD patients illustrated a similar scenario.Our study is aimed to propose a new approach for revealing the underlying mechanism of rTMS therapy and predicting the rTMS therapeutic effect, but a few limitations need further investigations. First, the sample size of this study was relatively small, and no AD patients were recruited in the sham group. We will expand the sample size to validate these findings in our future studies. A multicenter longitudinal design is needed, and an individualized method for target selection will be formulated. Second, all participants in this study lacked cerebrospinal fluid examinations to identify potential pathological biomarkers. Third, all testing in this study was performed at the same site. Multiple‐site trials may yield improved and more precise data, and the potential mechanism may be more complex.