Translational modelling of low and medium intensity transcranial magnetic stimulation from rodents to humans
Bolland, S. J.; Goryachev, M.; Opitz, A. J.; Tang, A.; Rodger, J.
Show abstract
BackgroundRodent models using subthreshold intensities of transcranial magnetic stimulation (TMS) have provided insight into the biological mechanisms of TMS but often differ from human studies in the intensity of the electric field (E-field) induced in the brain. ObjectiveTo develop a finite element method model as a guide for translation between low and medium intensity TMS rodent studies and high intensity TMS studies in humans. MethodsFEM models using three head models (mouse, rat, and human), and eight TMS coils were developed to simulate the magnetic flux density (B-field) and E-field values induced by three intensities. ResultsIn the mouse brain, maximum B-fields ranged from 0.00675 T to 0.936 T and maximum E-field of 0.231 V/m to 60.40 V/m E-field. In the rat brains maximum B-fields ranged from of 0.00696 T to 0.567 T and maximum E-fields of 0.144 V/m to 97.2 V/m. In the human brain, the S90 Standard coil could be used to induce a maximum B-field of 0.643 T and E-field of 241 V/m, while the MC-B70 coil induced 0.564 T B-field and 220 V/m E-field. ConclusionsWe have developed a novel FEM modelling tool that can help guide the replication of rodent studies using low intensity E-fields to human studies using commercial TMS coils. Modelling limitations include lack of data on dielectric values and CSF volumes for rodents and simplification of tissue geometry impacting E-field distribution, methods for mitigating these issues are discussed. A range of additional cross-species factors affecting the translation of E-fields were identified that will aid TMS E-field modelling in both humans and rodents. We present data that describes to what extent translation of brain region-specific E-field values from rodents to humans is possible and detail requirements for future improvement. A graphical abstract of the translational modelling pipeline from this study is provided below (Figure A.1). O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/591424v1_figA1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@5aa978org.highwire.dtl.DTLVardef@2c2128org.highwire.dtl.DTLVardef@13507c1org.highwire.dtl.DTLVardef@9044d6_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure A.1:C_FLOATNO A translational modelling pipeline describing the use of structural imaging data for FEM modelling purposes and the comparison of brain region-specific rodent and human LI-rTMS E-field values. C_FIG HighlightsO_LIClinical translation of rodent TMS studies is challenging due to the differences in coil and brain size and shape between rodents and humans. C_LIO_LIWe have built a FEM model for the accurate replication of TMS-derived E-fields validated in rodent models in multiple brain regions in humans. C_LIO_LIThis model is useful in designing stimulation parameters for humans based on rodent studies. C_LIO_LIThis model is a critical part of a translational pipeline for evidence based TMS. C_LI
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Computation of transcranial magnetic stimulation electric fields using self-supervised deep learning 96%
- A Systematic Review and Large-Scale tES and TMS Electric Field Modeling Study Reveals How Outcome Measure Selection Alters Results in a Person- and Montage-Specific Manner 96%
- Fast computational optimization of TMS coil placement for individualized electric field targeting 95%
Similar papers in this journal
- The rt-TEP tool: real-time visualization of TMS-Evoked Potential to maximize cortical activation and minimize artifacts 95%
- Designing and comparing cleaning pipelines for TMS-EEG data: a theoretical overview and practical example 93%
- 'RMT-Finder': an automated procedure to determine the Resting Motor Threshold for Transcranial Magnetic Stimulation 93%
Similar papers in this journal
- Group-Level Analysis of Induced Electric Field in Deep Brain Regions by Different TMS Coils 97%
- Characterizing an electronic-robotic targeting platform for precise and fast brain stimulation with multi-locus transcranial magnetic stimulation 95%
- An Adaptive H-Refinement Method for the Boundary Element Fast Multipole Method for Quasi-static Electromagnetic Modeling 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.