Transcranial magnetic stimulation (TMS), as a non-invasive neuromodulation technique, has been widely applied in the treatment of various neuropsychiatric disorders such as depression, anxiety, Parkinson’s disease, stroke, and chronic pain [1, 2, 3, 4]. Its fundamental principle is based on the law of electromagnetic induction: a brief, powerful, time-varying magnetic field generated by an electromagnetic coil placed on the scalp passes unimpeded through the skull and induces a secondary electric field (E-field) within the cerebral cortex [5]. This induced E-field is the “dose” of TMS, directly altering the membrane potential of neurons to modulate their activity and improve neurological function. When applied in a repetitive pattern (rTMS), it can induce lasting effects on cortical excitability and network connectivity, which form the basis of its therapeutic effects [6]. Therefore, the characteristics of the intracranial E-field, specifically its strength, spatial distribution, and orientation, are the key physical factors determining the biological effects and clinical efficacy of TMS [7, 8, 9, 10].

Although the clinical effectiveness of TMS has been confirmed, optimizing treatment protocols still faces numerous challenges, with the precise determination of effective stimulation intensity for different brain regions being a central issue [11]. Current clinical practice and numerous studies widely rely on the motor threshold (MT) as the “gold standard” for dose calibration. MT is a physiological metric defined as the lowest stimulation intensity that reliably evokes a motor evoked potential (MEP) of a specific amplitude in a contralateral hand muscle when stimulating the hand area of the primary motor cortex (M1). This intensity is typically expressed as a percentage of the maximum stimulator output (%MSO). Subsequently, the stimulation intensity for non-motor areas, such as the dorsolateral prefrontal cortex (DLPFC) commonly targeted in depression treatment, is set as a percentage of this MT (e.g., 120% MT) [12]. This practice is widespread because the MEP provides an objective, quantifiable physiological feedback, making it possible to standardize stimulation dosage across different individuals [13, 14, 15].

However, this “one-size-fits-all” approach remains debated, as it assumes that an intensity producing a threshold-level physiological effect at M1 will yield comparable target engagement at the DLPFC. Such an approach may conflate a physiologically convenient metric with the biophysically relevant dose at the therapeutic target, which could contribute to variability in target engagement and outcomes. This fundamental misalignment between convenience and relevance may be a major source of the large inter-individual variability in efficacy observed in clinical trials. The challenge stems from significant anatomical heterogeneity across cortical regions, which manifests in several ways. First, the scalp-to-cortex distance, along with the thickness of the skull, cerebrospinal fluid (CSF), and the cortex itself, varies across the head. These tissues have different conductive and attenuating properties, directly affecting the actual E-field strength that ultimately reaches the neurons [16]. Second, the cortical folding patterns (i.e., the morphology of gyri and sulci) differ vastly between brain regions [7]. M1 is typically located on the anterior wall of the central sulcus with a relatively regular shape, whereas the gyral-sulcal structure of the DLPFC is more complex. These intricate geometric shapes greatly influence the local E-field distribution and orientation. These inter-individual and inter-regional anatomical differences collectively lead to enormous variability in the induced E-field characteristics under a fixed %MSO [16], making it extremely difficult to achieve stable and comparable biological effects. These issues are particularly pronounced in the elderly population. On one hand, older adults are a key target demographic for TMS therapies (e.g., for treating age-related cognitive decline); on the other hand, the very neuroanatomical changes associated with aging are likely to intensify E-field variations between brain regions, effectively subjecting the MT-based dosing paradigm to a critical test of its generalizability.

In recent years, the development of computational modeling and simulation techniques has provided powerful tools for accurately investigating this issue. By constructing anatomically precise, personalized head models based on individual magnetic resonance imaging (MRI) and using numerical methods like the finite element method (FEM), it is possible to simulate the TMS-induced intracranial E-field distribution with high fidelity. This approach, known as computational dosimetry, precisely calculates the physical E-field dose applied to the brain based on individual anatomy, rather than simulating a virtual physiological response. Its validity has been extensively verified through comparisons with direct physiological measurements, such as MEPs [17]. Because the induced E-field itself is the key physical entity that determines the effects of TMS, computational dosimetry can bypass the confounding variable of individual physiological reactivity. This allows for the direct assessment and comparison of the physical dose delivered to different brain regions, offering an ideal pathway to resolve the shortcomings of current dose calibration methods.

In this context, we hypothesized that regional anatomical differences between M1 and the DLPFC, potentially amplified in older adults, would lead to systematic between-target differences in the induced E-field, not only in overall magnitude but also in the balance of orientation components. Prior computational dosimetry studies have highlighted substantial inter-individual and target-wise variability in TMS-induced E-fields, as well as sensitivity to coil placement and orientation [7, 11, 13, 14]. Building on this literature, our contribution is to quantify target-wise differences in an older-adult cohort and to explicitly bridge fixed-output comparisons with an MT-equivalent, M1-referenced scaling analysis that interrogates both E-field magnitude and component balance. To test this, we combined two complementary modeling perspectives: a fixed-output comparison to isolate anatomical effects, and an M1-referenced intensity-scaling analysis based on an MT-equivalent reference output to approximate how clinical protocols scale intensity from M1 to non-motor targets. Together, these analyses aim to clarify what aspects of the E-field can or cannot be matched across targets by intensity scaling alone, and to inform the development of more individualized, target-aware dosing strategies.

As a promising non-invasive neuromodulation technique, TMS offers a new therapeutic option for many neuropsychiatric disorders that respond poorly to conventional drug treatments [1, 2, 3, 4]. By targeting specific brain regions, TMS can modulate neural activity and alleviate symptoms. However, the widespread clinical practice of using the MT measured at M1 to set the stimulation intensity for non-motor areas like the DLPFC remains debated, given substantial anatomical and physiological differences across cortical regions [10, 13, 14, 15]. Using advanced computational E-field modeling, this study directly compared E-field characteristics at M1 and DLPFC under standardized stimulation conditions, providing a biophysical basis for refining dosing strategies.

The core finding of this study is that, under identical physical stimulation conditions, the E-field magnitude delivered to the DLPFC is significantly lower than that delivered to M1. Both on the cortical surface and within the gray matter volume, the mean and robust maximum E-field magnitudes were significantly lower in the DLPFC. This difference can be attributed to the combined effect of multiple anatomical factors. First, M1 is typically located near the central sulcus with relatively regular cortical folding, whereas the DLPFC has a more complex gyral-sulcal morphology, including deeper sulci and more tortuous gray-white matter interfaces [7], which can disperse induced currents and alter local E-field magnitude distributions. Second, the DLPFC is located more anteriorly, and regional differences in local skull properties and coil-to-cortex distance may exist across individuals and targets. Given the high resistivity of the skull and distance-dependent attenuation, these factors can jointly reduce the induced E-field magnitude. In addition, conductivity contrasts across tissues can substantially shape field distributions [17]. Because CSF is far more conductive than gray matter, induced currents may preferentially flow through the CSF as a low-resistance pathway, producing a “shunting effect” that reduces the effective E-field magnitude within cortical gray matter. Meanwhile, M1 and DLPFC differ in the relative proportions and geometric configurations of gray matter, white matter, and CSF; prior work has suggested that the prefrontal cortex may be covered by a thicker CSF layer in some individuals [7], which could further exacerbate E-field attenuation at DLPFC. The elderly cohort in our study offers additional insight into these mechanisms. Aging is often accompanied by cortical atrophy, increasing CSF volume in sulci and the subarachnoid space [28]. Therefore, the reduced DLPFC E-field magnitude observed in our study likely reflects the combined influence of intrinsic prefrontal anatomical complexity and age-related structural changes. This mechanistic account helps explain the observed attenuation and suggests that, in older adults, M1-referenced linear intensity settings may benefit from more refined, individualized parameter adjustment to compensate for this additional biophysical attenuation.

The most innovative finding of this study is the revelation of a specific deficit in the tangential electric field component at the DLPFC. This result has important mechanistic implications, as it links the macroscopic field properties to neuronal activation mechanisms. While E-field magnitude is a key determinant, its spatial orientation is also critical in shaping which neural elements are preferentially engaged [29]. Microscopically, the cerebral cortex contains pyramidal neurons that are predominantly oriented perpendicular to the cortical surface [7]. Both theory and experiments suggest that the normal component (perpendicular to the cortical surface) is strongly related to activation mechanisms underlying MEP generation at M1 [7, 30]. However, the cerebral cortex also contains many other neuron types, particularly various interneurons, which constitute up to 20–30% of the neurons in the prefrontal cortex [31, 32, 33]. The axons and dendrites of these interneurons may run more parallel to the cortical surface, potentially increasing their sensitivity to the tangential E-field component [5]. In the DLPFC, modulation of these complex local circuit elements is often hypothesized to contribute to mood and cognitive regulatory effects of TMS, rather than relying solely on activation of a single class of projection neuron [11, 34, 35, 36]. These GABAergic interneurons play a crucial role in shaping network activity, gating information flow, and supporting neuroplasticity [34].

In this study, while the normal E-field component was comparable between M1 and DLPFC, the tangential component was significantly weaker in the DLPFC. This finding has important mechanistic implications. At M1, the primary measurable output (MEP) is closely linked to activation of corticospinal pyramidal neurons, for which a strong normal component is thought to be effective [7, 9]. By contrast, therapeutic effects at DLPFC likely depend more on modulation of complex local circuits containing diverse interneuron populations [34, 35, 36]. Therefore, a reduced tangential component at DLPFC suggests that an intensity calibrated using an M1-derived physiological reference may not ensure comparable target engagement across regions, particularly for circuit elements that may be more sensitive to tangential fields. This interpretation is consistent with multiscale modeling studies showing that different neuronal subtypes can exhibit different activation thresholds and orientation sensitivities [37]. In this sense, MT-based dosing uses a physiological output largely driven by M1-relevant activation mechanisms (MEP) to calibrate intensity, and then applies it to a target (DLPFC) whose effects may depend on a different balance of E-field components. Such a biophysical mismatch may contribute to variability in stimulation effects and inter-individual differences in rTMS response.

These fixed-output results highlight a target-wise biophysical asymmetry between M1 and DLPFC that cannot be inferred from MEP-based calibration alone. Because clinical rTMS protocols commonly scale stimulation intensity from an M1-derived reference, we next examined whether intensity scaling can reconcile the observed differences in overall E-field magnitude and component balance at DLPFC. Importantly, our MT-equivalent reference output analysis bridges the fixed-output comparisons with the typical M1-referenced intensity scaling used in clinical rTMS. In this analysis, increasing intensity reduced the overall E-field magnitude gap between DLPFC and the M1 reference level, with approximate magnitude parity occurring around 110% of the MT-equivalent reference output. However, the same scaling also revealed a component trade-off: while 120% scaling brought the tangential component at DLPFC closer to the M1 reference level, the normal component increased significantly beyond the M1 reference level. Together, these results indicate that intensity-only linear scaling may be insufficient to simultaneously match overall E-field magnitude and component balance at DLPFC across individuals, likely due to target-specific anatomical determinants.

Taken together, our findings provide a practical biophysical rationale for moving toward target-specific, E-field–informed parameter selection in TMS applications. These observations are consistent with prior modeling work emphasizing that intensity scaling alone cannot fully compensate for target-specific anatomy and coil–cortex geometry [11, 24, 38], and they reinforce the rationale for incorporating E-field–informed targeting and dosing into future prospective studies. Rather than directly demonstrating under-dosing in routine clinical practice, the present results suggest that M1-referenced dosing may not ensure comparable E-field magnitude at the DLPFC and component balance across individuals. This motivates strategies that jointly consider intensity and coil orientation (and, where available, individualized modeling) to better align stimulation parameters with target characteristics, especially in older adults. More broadly, the results support integrating personalized, model-based simulation methods into treatment planning as a component of precision neuromodulation [38]. Notably, advances in faster and near real-time E-field modeling are making such approaches increasingly feasible in practical workflows [39]. Future studies combining model-based predictions with physiological measures (e.g., MEP-related benchmarks for M1 and TMS–EEG/behavioral readouts for DLPFC) and clinical outcomes will be essential to validate and refine these dosing strategies.

Using personalized head model simulations, this study shows that under the same physical stimulator output, TMS-induced E-fields at M1 and DLPFC differ in both magnitude and component balance. Specifically, overall E-field magnitude at DLPFC is lower, and the tangential component is significantly reduced relative to M1. Using an MT-equivalent reference output framework, intensity scaling reduced the overall magnitude gap (with approximate parity around 110%), but a component trade-off remained: scaling that brought the tangential component closer to the M1 reference tended to increase the normal component beyond the M1 reference. Together, these biophysical findings suggest that M1-referenced intensity scaling may not ensure comparable DLPFC E-field magnitude and component balance across individuals, highlighting the potential value of target-specific, E-field–informed parameter selection. Future work combining individualized modeling with physiological and clinical validation will be essential to determine whether such strategies improve reliability and outcomes in TMS applications.

The MRI datasets analyzed during the current study are publicly available in the Science Data Bank repository. Specifically, data were sourced from the Chinese Human Connectome Project (CHCP), which can be accessed via the following link: https://www.scidb.cn/en/detail?dataSetId=f512d085f3d3452a9b14689e9997ca94 (DOI: 10.11922/sciencedb.01374). The SimNIBS 4.1 software used for the simulations is open-source and publicly available at https://simnibs.github.io/simnibs/.

YW and SQ designed the research study. YW performed the research, including methodology development, software implementation, data curation, and visualization. YW and YL analyzed the data. YL, HW, and CW conducted the validation. HW and CW provided resources for the study. YW prepared the original draft of the manuscript. SQ provided supervision, project administration, and funding acquisition. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.

This research is a secondary analysis of a publicly available and fully anonymized dataset. As such, no new ethics approval was required for the computational modeling work performed in this study.

We gratefully acknowledge the data provided by the Chinese Human Connectome Project (CHCP, PI: Jia-Hong Gao), funded by the Beijing Municipal Science & Technology Commission, the Chinese Institute for Brain Research (Beijing), the National Natural Science Foundation of China, and the Ministry of Science and Technology of China.

This research was funded by the National Key R&D Program of China on Active Health and Aging Technologic Solutions, grant numbers 2022YFC3600501 and 2022YFC3600500.

The authors declare no conflict of interest.