No abstract

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Repetitive transcranial magnetic stimulation (rTMS) has gained considerable importance in the treatment of disorders, e.g. depression. However, it is not yet understood how rTMS alters brain's functional connectivity. Here we report the changes captured by resting state functional magnetic resonance imaging (rsfMRI) within the first hour after 10Hz rTMS in (1) nodes, where the strongest functional connectivity of regions is seen, and (2) boundaries, where functional transitions between regions occur. We use support vector machines (SVM), a widely used machine learning algorithm that has been proven to be robust and effective, for the classification and characterization of time intervals of major changes in node and boundary maps, while respecting the variability between subjects. Particularly in the posterior cingulate cortex and precuneus, our results reveal that the changes in connectivity at the boundaries are slower and more complex than in those observed in the nodes, but of similar magnitude according to accuracy confidence intervals. As the network boundaries are under studied in comparison to nodes in connectomics research, our results highlight their contribution for functional adjustments to rTMS.