Evaluation of neuronal activation thresholds for low-frequency electromagnetic exposure using morphologically...

International guidelines for low‑frequency electromagnetic field exposure (LF EMF) are primarily intended to prevent substantiated adverse effects. In the frameworks, limits on internal electric fields are linked to external exposure levels through computational dosimetry. However, the relationship between internal electric fields and these adverse effects remains incompletely understood. In particular, current approaches often overlook the morphological complexity and diversity of cortical neurons, which may limit the realism of neuronal activation estimates used to support these assessments. This study aimed to evaluate LF EMF-induced neural activation using morphologically realistic neuron models representing all cortical layers. Approach. Twenty-five morphologically realistic neuron models were embedded within 11 detailed human head models. The internal electric fields were simulated for uniform magnetic field exposures (100 Hz-100 kHz) along the three anatomical directions, and excitation thresholds were computed using a multi‑scale framework combining voxel‑based dosimetry with biophysical neuron simulations. A real‑world exposure scenario involving a child near an acousto‑magnetic article‑surveillance deactivator was also analyzed. Main results. Excitation threshold varied across cell type, morphology, cortical location, subject anatomy, frequency, and exposure direction, with L2/3 pyramidal, L4 basket, and L5 thick‑tufted pyramidal cells showing the lowest thresholds. Despite this variability, all simulated thresholds were conservative with respect to the basic restrictions and dosimetric reference limits set by IEEE ICES and ICNIRP. The smallest margin occurred at 100 kHz, where the threshold remained a factor of 2.8 above the corresponding limit. Significance. These findings indicate that current LF EMF exposure limits remain conservative when evaluated using highly detailed, morphology‑based CNS activation models.