Network-based near-scalp personalized brain stimulation targets

Background: Functional connectivity (FC) is often used to identify personalized targets for transcranial magnetic stimulation (TMS). However, existing methods often overlook individual differences in whole-cortex network organization. Furthermore, in some personalized TMS protocols, a lower stimulation intensity is used for targets closer to the scalp. Therefore, near-scalp targets might improve patient tolerance. Objective: We develop an algorithm to simultaneously optimize FC and scalp proximity for personalized target localization. Methods: We use the multi-session hierarchical Bayesian model (MS-HBM) to estimate high-quality individual-specific cortical networks. A tree-based algorithm is then used to select the optimal target location. By essentially having no parameter to tune, our framework might improve generalizability across populations. We compare our approach to existing "cluster" and "cone" algorithms in terms of scalp proximity, test-retest reliability, and FC to brain regions implicated in depression. Results: In two test-retest datasets of healthy individuals from the United States and Singapore, tree-based MS-HBM reliably identifies personalized TMS targets for depression in close proximity to the scalp. These targets compare favorably with cone and cluster targets in terms of reliability, scalp proximity, and FC to the subgenual anterior cingulate cortex in new out-of-sample MRI sessions from the same individuals. Compared with the cluster algorithm, tree-based MS-HBM targets hypothetically reduce stimulation intensity by 12% using a linear intensity adjustment protocol. To demonstrate versatility, we apply the same algorithm, without having to tune any parameter, to identify personalized TMS targets for anxiety. Conclusion: The tree-based MS-HBM algorithm provides a robust, generalizable framework to estimate near-scalp personalized targets across different populations.