Quantitative measures for Transcranial Magnetic Stimulation (TMS) intensity are needed to ensure safe and consistent application in therapeutic and research settings. However, resting motor thresholds (rMTs), commonly used to determine stimulation intensity, depend on the coil used. Unless motor mapping and treatment coils are identical, re-thresholding is necessary, increasing patient discomfort and potentially introducing variability across studies. These considerations raise an unresolved fundamental question: does individual rMT reflect a consistent cortical electric field (E-field) magnitude independent of coil geometry? We tested the hypothesis that rMT corresponds to a coil-invariant cortical E-field magnitude and evaluated a computational method for predicting stimulator output across different coils using a reference rMT. Thirteen healthy, right-handed participants were recruited; ten were included in the primary analysis. Participants underwent TMS with two figure-of-eight coils of different sizes. E-field distributions were simulated using a fast multipole boundary element method, in free space and within personalized MRI-based head models. rMT prediction accuracy was compared between a detailed five-layer and a simplified three-layer head model, and both were evaluated against direct rMT scaling using the reference coil. The personalized E-field-based approach significantly improved rMT prediction accuracy over direct scaling (p < 0.001). The root-mean-square error (RMSE) was 1.26% and 1.32% of maximum stimulator output (MSO) for detailed and simplified models, versus 6.1% MSO for direct scaling. Individual rMT corresponds to a constant cortical E-field magnitude ratio across coil types. E-field-based prediction offers a more accurate, coil-independent method for standardizing TMS intensity, reducing the need for repeated thresholding.

BACKGROUND:Transcranial Magnetic Stimulation (TMS) is an established non-invasive neuromodulation technique, with growing evidence suggesting that targeting multiple interconnected nodes may enable more selective network-level control. However, multisite stimulation remains constrained by the fixed field geometry and limited focality of conventional coils. OBJECTIVE:To introduce and characterize a modular multichannel transcranial magnetic stimulation (mTMS) array capable of electronically steering the induced electric field (E-field) without mechanical coil movement. METHODS:The array consists of two custom-made 3-axis TMS coils arranged with a slight tilt to approximate head curvature and enhance stimulation depth and efficiency. This modular architecture allows flexible adjustment of coils spacing and orientation. Computational simulations and in vivo experiments demonstrate that independently driven coil elements can be combined to form distinct "virtual coils", enabling controlled electronic shifts of the E-field hotspot. RESULTS:Using a physical-versus-electronic hotspot displacement paradigm with repeated resting motor threshold (rMT) estimation, we show that electronic E-field shifts of ±1 cm produce effects comparable to physically moving the coil. Computational modeling analysis confirms that the electronically synthesized virtual coil configurations elicit systematic E-field shifts in precentral gyrus that are spatially consistent with the measured rMT at each physical coil array location. The system achieved functional resolution consistent with the known spatial accuracy of TMS, with variability within expected limits of neuronavigation and calibration errors. CONCLUSION:These findings establish the feasibility and physiological relevance of electronically controlled E-field steering using a modular coil array. This platform provides a scalable foundation for next generation mTMS systems supporting multifocal stimulation of distributed brain networks.

Higher-order cognitive and affective functions are supported by large-scale networks in the brain. Dysfunction in different networks is proposed to associate with distinct symptoms in neuropsychiatric disorders. However, the specific networks targeted by current clinical transcranial magnetic stimulation (TMS) approaches are unclear. While standard-of-care TMS relies on scalp-based landmarks, recent FDA-approved TMS protocols use individualized functional connectivity with the subgenual anterior cingulate cortex (sgACC) to optimize TMS targeting. Leveraging previous work on precision network estimation and modeling of the TMS electric field (E-field), we asked whether various clinical TMS approaches target different functional networks between individuals. Results revealed that modeled homotopic scalp positions (left F3 and right F4) target different networks within and across individuals, and right F4 generally favors a right-lateralized control network. TMS coil positions over the dorsolateral prefrontal cortex (dlPFC) zone anticorrelated with the sgACC most frequently target a network coupled to the ventral striatum (reward circuitry) but largely miss that network in some individuals. We further illustrate how modeling can be used to retrospectively assess the estimated targets achieved in prior TMS sessions and also used to prospectively provide coil positions that can target distinct closely localized dlPFC network regions with spatial selectivity and maximal E-field intensity. In a final study, precision targeting was found to be feasible in participants with Major Depressive Disorder using data derived from a single low-burden MRI session suggesting the methods are applicable to translational efforts where limiting patient burden and ensuring robustness are critical.

Working memory (WM), short term maintenance of information for goal directed behavior, is essential to human cognition. Identifying the neural mechanisms supporting WM is a focal point of neuroscientific research. One prominent theory hypothesizes that WM content is carried in "activity-silent" brain states involving short-term synaptic changes. Information carried in such brain states could be decodable from content-specific changes in responses to unrelated "impulse stimuli". Here, we used single-pulse transcranial magnetic stimulation (spTMS) as the impulse stimulus and then decoded content maintained in WM from EEG using multivariate pattern analysis (MVPA) with robust non-parametric permutation testing. The decoding accuracy of WM content significantly enhanced after spTMS was delivered to the posterior superior temporal cortex during WM maintenance. Our results show that WM maintenance involves brain states, which are activity silent relative to other intrinsic processes visible in the EEG signal.

Working memory (WM), short term maintenance of information for goal directed behavior, is essential to human cognition. Identifying the neural mechanisms supporting WM is a focal point of neuroscientific research. One prominent theory hypothesizes that WM content is carried in "activity-silent" brain states involving short-term synaptic changes. Information carried in such brain states could be decodable from content-specific changes in responses to unrelated "impulse stimuli". Here, we used single-pulse transcranial magnetic stimulation (spTMS) as the impulse stimulus and then decoded content maintained in WM from EEG using multivariate pattern analysis (MVPA) with robust non-parametric permutation testing. The decoding accuracy of WM content significantly enhanced after spTMS was delivered to the posterior superior temporal cortex during WM maintenance. Our results show that WM maintenance involves brain states, which are activity silent relative to other intrinsic processes visible in the EEG signal.

Combining brain imaging methods with non-invasive brain stimulation such as transcranial magnetic stimulation (TMS) is a rapidly expanding field with the potential to drastically improve the understanding of brain function. However, currently there is no generally applicable hardware solution optimized for these types of acquisitions. To make concurrent TMS/fMRI experiments at 3 T feasible without sacrificing imaging quality, we have designed, constructed, and tested the first of its kind "RF Cap": a 26-channel flexible RF coil cap. The RF Cap achieves full brain coverage with high sensitivity while allowing the administration of TMS at most targets over the scalp with an easy setup and possibility of using a neuronavigation system. The RF Cap consists of a FLEXIBLE and a RIGID part. The FLEXIBLE part is a neoprene cap with 26 flexible RF coaxial cable loops sewn onto it and distributed following a soccer ball layout. The RF elements were interfaced using flexible PCBs and incorporating a BALUN to minimize common modes on the short cables connecting the elements to their preamplifiers placed on the RIGID part. This solution provides a user-friendly approach for concurrent TMS/fMRI acquisitions while ensuring optimal patient comfort. We show that the RF Cap offers at least 4 times more SNR than a birdcage coil at the center of the brain and 10-16 times more SNR on the cortex. The effects of the TMS on the SNR of the RF Cap are between 10% and 25% loss over the region where the TMS coil is placed. The RF Cap has the potential to transform concurrent TMS/fMRI into a practical and useful neuroscientific tool as well as to pave the way for future clinical applications.