Cortical Stimulation Strength and sgACC Connectivity Shape Neuro‑Cardiac Responses to Prefrontal TMS

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Abstract

Autonomic dysregulation is a core feature of major depressive disorder and a promising physiological readout for early signs of clinical effectiveness of therapeutic TMS targeting prefrontal regions. Yet the stimulation dose and network properties required to optimally engage autonomic circuits remain elusive. Here, we applied a cortex- and circuit-centered dosing framework combining resting-state fMRI, simulations of the TMS-induced electric field, and electrocardiography acquired during repetitive TMS of six left dorsolateral prefrontal cortex (DLPFC) targets in healthy adults. Heart–brain coupling (HBC), defined as spectral power in the cardiac response locked to the TMS onset frequency, was modeled as a function of either conventional motor-threshold-based dose or cortical electric-field strength, with and without DLPFC connectivity to the subgenual anterior cingulate cortex (sgACC). Across linear and nonlinear models, cortical electric-field dose explained substantially more variance in HBC, indicating that prefrontal TMS dosing is best assessed at the cortical level. Incorporating sgACC connectivity further improved model fit and revealed site-specific electric-field-by-connectivity interactions, with the strongest and most reliable HBC modulation at DLPFC regions anticorrelated with sgACC. Voxel-wise TMS mapping identified individualized cortical hotspots spatially overlapping with sgACC-anticorrelated tissue. Notably, whereas negative connectivity showed the most robust and monotonic effects, nonlinear dose-response analyses suggested a confined stimulation range over which positively connected sites also exhibited HBC modulation. Together, these findings position HBC as a network-sensitive physiological readout of DLPFC–sgACC–vagal engagement and offer a principled answer to where and how much to stimulate, supporting an electric-field- and connectivity-informed dosing framework for refining prefrontal TMS in depression-related autonomic dysregulation.
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Abstract Autonomic dysregulation is a core feature of major depressive disorder and a promising physiological readout for early signs of clinical effectiveness of therapeutic TMS targeting prefrontal regions. Yet the stimulation dose and network properties required to optimally engage autonomic circuits remain elusive. Here, we applied a cortex- and circuit-centered dosing framework combining resting-state fMRI, simulations of the TMS-induced electric field, and electrocardiography acquired during repetitive TMS of six left dorsolateral prefrontal cortex (DLPFC) targets in healthy adults. Heart–brain coupling (HBC), defined as spectral power in the cardiac response locked to the TMS onset frequency, was modeled as a function of either conventional motor-threshold-based dose or cortical electric-field strength, with and without DLPFC connectivity to the subgenual anterior cingulate cortex (sgACC). Across linear and nonlinear models, cortical electric-field dose explained substantially more variance in HBC, indicating that prefrontal TMS dosing is best assessed at the cortical level. Incorporating sgACC connectivity further improved model fit and revealed site-specific electric-field-by-connectivity interactions, with the strongest and most reliable HBC modulation at DLPFC regions anticorrelated with sgACC. Voxel-wise TMS mapping identified individualized cortical hotspots spatially overlapping with sgACC-anticorrelated tissue. Notably, whereas negative connectivity showed the most robust and monotonic effects, nonlinear dose-response analyses suggested a confined stimulation range over which positively connected sites also exhibited HBC modulation. Together, these findings position HBC as a network-sensitive physiological readout of DLPFC–sgACC–vagal engagement and offer a principled answer to where and how much to stimulate, supporting an electric-field- and connectivity-informed dosing framework for refining prefrontal TMS in depression-related autonomic dysregulation. Competing Interest Statement The authors have declared no competing interest.

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License: CC-BY-NC-ND-4.0