Network-based Near-Scalp Personalized Brain Stimulation Targets

Abstract 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, lower stimulation intensity is used for targets closer to the scalp, which may improve patient tolerance. Here, we develop an algorithm to simultaneously optimize FC and scalp proximity for target localization. We first 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. With essentially no parameter to tune, our framework may potentially improve generalizability across populations. We compare our approach to existing “cluster” and “cone” algorithms. 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 near the scalp. Tree-based MS-HBM targets compare favorably with cluster and cone targets in terms of reliability, scalp proximity, and FC to the subgenual anterior cingulate cortex (sACC) in new out-of-sample MRI sessions. To demonstrate versatility, the same algorithm identifies personalized anxiety targets without tuning any parameter. In patients with treatment-resistant depression, tree-based MS-HBM targets compare favorably with cluster and cone targets in terms of reliability, scalp proximity, and sACC FC, hypothetically reducing stimulation intensity by 15% and 5% respectively. MS-HBM also exhibits the best (most negative) electric-field hotspot sACC FC and highest reliability in induced electric fields. Overall, tree-based MS-HBM provides a robust, generalizable framework to estimate near-scalp personalized targets across populations. Competing Interest Statement RK and LQRO are co-founders of B1Neuro, a startup commercializing fMRI-based targeting software. BTTY and PCT serve as clinical advisors to B1Neuro. RK, LQRO, and BTTY hold shares in B1Neuro, and PCT holds non-remunerative shares in the company. RK, AX, CLA, XWT, MDF, SS, PCT, and BTTY might financially benefit from a pending patent covering the targeting algorithm used in the current study filed by the National University of Singapore (NUS). B1Neuro may license this patent from NUS. All other authors declare no competing interests. Footnotes In this revision, we have included an additional clinical dataset comprising 43 patients with treatment-resistant depression.