The cerebral cortex is organized into structurally and functionally segregated networks, enabling the human brain to process information highly efficiently. Transcranial magnetic stimulation (TMS), in combination with electroencephalography (EEG), offers a non-invasive approach to probing brain networks, revealing cortical excitability and causal connectivity. However, this method faces two significant challenges: (a) ensuring the quality of TMS-evoked potentials (TEPs) to maximize information gain, often requiring comprehensive cortical mapping, and (b) eliciting the response from the network of interest and not from adjacent cortical sites. Existing TMS targeting approaches frequently fail to precisely stimulate functionally relevant cortical areas, hindering treatment efficacy and the identification of biomarkers. The presented protocol integrates precise cortical mapping to acquire artifact-free TEPs, enabling reliable and reproducible measurements of early TEP components. This precision improves sensitivity to subtle neurophysiological variations and strengthens correlations with clinical phenotypes, supporting biomarker discovery in neuropsychiatric disorders. The proposed protocol utilizes structural, functional, and diffusion magnetic resonance imaging (MRI) to identify cortical patches belonging to the network of interest. Anatomical parcellation, functional connectivity, and real-time tractography are applied to locate areas with strong connectivity to other brain regions associated with the target network. The resulting personalized cortical clusters define the initial stimulation targets. TMS–EEG mapping is then employed to optimize TMS parameters by localizing cortical areas with high excitability, enhancing neuronal response magnitude while reducing non-neuronal noise, including muscle artifacts, decay, and other confounding factors affecting early TMS–EEG responses. A systematic exploration of the cortical mantle is conducted, adjusting stimulation location, orientation, and intensity, with continuous data quality monitoring through real-time visualization of averaged TEPs. TMS parameters producing artifact-free responses with clearly discernible early TEP components are selected for data collection. This article introduces the neuroimaging-guided TMS–EEG mapping technique and highlights the methodological advancements and benefits achievable through its application.

Background Spontaneous cortical oscillations have been shown to modulate cortical responses to transcranial magnetic stimulation (TMS). However, whether these oscillations influence cortical effective connectivity is largely unknown. We conducted a pilot study to set the basis for addressing how spontaneous oscillations affect cortical effective connectivity measured through TMS-evoked potentials (TEPs). Methods We applied TMS to the left primary motor cortex and right pre-supplementary motor area of three subjects while recording EEG. We classified trials off-line into positive- and negative-phase classes according to the mu and beta rhythms. We calculated differences in the global mean-field amplitude (GMFA) and compared the cortical spreading of the TMS-evoked activity between the two classes. Results Phase affected the GMFA in four out of 12 datasets (3 subjects × 2 stimulation sites × 2 frequency bands). Two of the observed significant intervals were before 50 ms, two between 50 and 100 ms, and one after 100 ms post-stimulus. Source estimates showed complex spatial differences between the classes in the cortical spreading of the TMS-evoked activity. Conclusions TMS-evoked effective connectivity seems to depend on the phase of local cortical oscillations at the stimulated site. This work paves the way to design future closed-loop stimulation paradigms.

Determining the optimal stimulation target for motor responses (motor hotspot) and the required intensity for reliably eliciting said responses (motor threshold) are common procedures in transcranial magnetic stimulation (TMS) research and treatments. However, the procedures for determining them are user-dependent, slow, and lack standardization, leading to long stimulation sessions with potentially inadequate outcomes. Partially automated algorithms for determining the motor threshold have been developed, but the motor hotspot is still largely mapped by hand. Automating the hotspot mapping will accelerate the process and improve standardization and accuracy. We developed a fully automated algorithm for finding the motor hotspot with multi-locus TMS and Bayesian optimization. Tested online in five healthy participants, the algorithm located motor hotspots with (mean ± 95% CI) 2.1 ± 0.7 mm and 6 ± 2° difference from the global best target with only (mean) 47 stimuli. This is a significant improvement from previous motor-mapping algorithms, which do not optimize for stimulation location and orientation simultaneously. This accurate, fast, and user-independent procedure paves the way for faster experimental processes and more streamlined clinical applications.

A key limitation of transcranial magnetic stimulation (TMS) is the lack of real-time, individualized feedback about where the brain is being stimulated, leading to inaccurate assumptions about the underlying affected areas. This uncertainty contributes to poor reproducibility, limited interpretability, and variable efficacy across individuals and sessions. We aimed to develop and validate a neuronavigation system with real-time electric field (E-field) computation and visualization on realistic head models, with extended support for multi-locus TMS (mTMS). We designed and built a software framework that integrates an E-field solver with the InVesalius navigation software, introducing a GUI and a workflow that support real-time E-field visualization and quantitative analysis. We characterized our E-field neuronavigation performance and demonstrated it in vivo using a commercial figure-of-eight coil and a robotic-guided 5-coil mTMS. We also quantified precision and accuracy errors, spatial root-mean-square error (RMSE), and maximum absolute gain (MAG). Real-time visualization of electronically shifted E-fields without moving the coil was demonstrated by mapping the precentral gyrus with mTMS and recording motor evoked potentials (MEPs) from finger muscles. The time required for building the boundary element models was 27 s, and the E-field module latency during navigation was approximately 24 ms per coil position. In vivo estimates of E-field distribution during TMS coil repositioning showed low precision errors (0.9-2%) and accuracy errors (2.2-4.9%). MAG values indicated consistent E-field amplitudes relative to reference fields, and RMSE values remained low (< 2.2 V/m) with consistent MEP responses. In mTMS visualization, the MEP amplitude decreased as the electronically shifted E-field moved away from the hotspot. Our system enables real-time, individualized E-field visualization during TMS, accounting for cortical folding and offering features that simplify, standardize, and improve the reproducibility of the TMS process.