Resistor-Capacitor Circuit Model for Predicting TMS Propagation in the Human Connectome
Presented During:
Saturday, June 28, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
Great Hall
Poster No:
1420
Submission Type:
Abstract Submission
Authors:
Yihang Jiao1, Caio Seguin2, Martin Tik3, Sina Mansour4, Robin Cash5, Andrew Zalesky2
Institutions:
1The University of Melbourne, Melbourne, VIC, 2Systems Lab, Department of Psychiatry, The University of Melbourne, Melbourne, Australia, 3Medical University of Vienna, Wien, Vienna, 4National University of Singapore, Singapore, Singapore, 5University of Melbourne, Torquay, VIC
First Author:
Co-Author(s):
Andrew Zalesky
Systems Lab, Department of Psychiatry, The University of Melbourne
Melbourne, Australia
Systems Lab, Department of Psychiatry, The University of Melbourne
Melbourne, Australia
Introduction:
Understanding and predicting signal propagation of Transcranial Magnetic Stimulation (TMS) through the human connectome would be immensely useful across research and clinical contexts. To date, neural mass and field models as well as graph-theoretic measures have been employed (Bortoletto et al., 2015; Gollo et al., 2017; Momi et al., 2021; Seguin et al., 2023), but they face challenges in balancing spatial resolution and computational efficiency, particularly when applied to large-scale brain networks. Here, we developed a novel Resistor-Capacitor (RC) circuit model to predict TMS-induced signal propagation across the human connectome. We compared cortical maps of model-predicted TMS-induced activation patterns to those measured in a previous interleaved TMS-fMRI experiment in which triplets of 10Hz stimulation pulses were targeted to the dorsolateral prefrontal cortex (DLPFC) (Tik et al., 2023). Our model provides dynamic, time-resolved cortical maps of the stimulation-induced brain activity at high spatial resolution, which accord with fMRI measurements.
Methods:
Our RC model is based on a group-average high-resolution human connectome (~9,000 cortical vertices), inferred from tractography and diffusion MRI (Figure 1). Connectome nodes are modelled as capacitors, connections in the connectome represent resistors. Initial voltages are assigned to each capacitor based on TMS electric field (E-field) simulations from finite element modeling. Propagation dynamics are modeled using Kirchhoff's laws at each node to establish a system of ordinary differential equations expressing voltages and currents at each connectome node as a function of time. Connectome spatial smoothing was performed on high-resolution connectome data (Mansour et al., 2022). The Spearman correlation coefficient and the spin test were used to compare the cortical map of model-predicted TMS-induced brain activation to those inferred empirically from previous TMS-fMRI experiment (Tik et al., 2023). Here, we focus on stimulation targeted to the DLPFC, but our model can yield predictions for any stimulation target.
Results:
Our RC model revealed detailed spatial propagation patterns beginning at the DLPFC and spreading across the cortical surface (Figure 2A). These time-resolved cortical patterns were summarized across time to yield a single time-averaged cortical map, which was correlated to the fMRI-inferred cortical map (Figure 2B and 2C). A significant correlation (e.g., r = 0.22 for maximum current pattern with a spin test at p<0.005) was found between our predictions and the fMRI-inferred cortical maps. Our models also revealed that TMS stimulation can propagate into regions like supramarginal gyrus (SMG) and anterior cingulate cortex (ACC) through the structural connectomes. Notably, incorporating auditory cortex stimulation to reflect auditory input to model the impact of the TMS clicking sound improved model alignment with TMS-fMRI data (Spearman correlation: r = 0.33).
Conclusions:
Our RC circuit model offers a robust and efficient framework for simulating TMS propagation across the human connectome, effectively balancing high-resolution accuracy with computational efficiency. It enables detailed investigations into network-level dynamics and reveals the involvement of additional regions in fMRI signals beyond the primary stimulation target. Future applications of our model include optimizing TMS targets and parameters in silico and optimizing TMS protocols for therapeutic applications.
Brain Stimulation:
Non-invasive Magnetic/TMS
TMS 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Keywords:
Computing
FUNCTIONAL MRI
Modeling
Transcranial Magnetic Stimulation (TMS)
Other - Structural Connectome; Circuit Model
1|2Indicates the priority used for review
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Provide references using APA citation style.
Gollo, L. L., Roberts, J. A., & Cocchi, L. (2017). Mapping how local perturbations influence systems-level brain dynamics. Neuroimage, 160, 97-112.
Mansour, L. S., Seguin, C., Smith, R. E., & Zalesky, A. (2022). Connectome spatial smoothing (CSS): Concepts, methods, and evaluation. Neuroimage, 250, 118930.
Momi, D., Ozdemir, R. A., Tadayon, E., Boucher, P., Shafi, M. M., Pascual-Leone, A., & Santarnecchi, E. (2021). Network-level macroscale structural connectivity predicts propagation of transcranial magnetic stimulation. Neuroimage, 229, 117698.
Seguin, C., Jedynak, M., David, O., Mansour, S., Sporns, O., & Zalesky, A. (2023). Communication dynamics in the human connectome shape the cortex-wide propagation of direct electrical stimulation. Neuron, 111(9), 1391-1401 e1395.
Tik, M., Woletz, M., Schuler, A. L., Vasileiadi, M., Cash, R. F. H., Zalesky, A., Lamm, C., & Windischberger, C. (2023). Acute TMS/fMRI response explains offline TMS network effects - An interleaved TMS-fMRI study. Neuroimage, 267, 119833.