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Sunday, July 19 • 8:00pm - 9:00pm
P70: Influence of anatomical connectivity and intrinsic dynamics in a connectome based neural mass model of TMS-evoked potentials

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Neda Kaboodvand
, John Griffiths

Perturbation via electromagnetic stimulation is a powerful way of probing neural systems to better understand their functional organization. One of the most widely used neurostimulation techniques in human neuroscience is transcranial magnetic stimulation (TMS) with concurrently recorded electroencephalography (EEG). The immediate EEG responses to single-pulse TMS stimulation, termed TMS-evoked potentials (TEPs), are spatiotemporal waveforms in EEG sensor- or source-space[1]. TEPs display several characteristic features, including i) rapid wave-like propagation away from the primary stimulation site, and ii) multiple volleys of recurrent activity, that continue for several hundred milliseconds following the stimulation pulse. These TEP patterns reflect reverberant activity in large-scale cortico- cortical and cortico-subcortical brain networks, and have been used to study neural excitability in a wide variety of research contexts, including sleep, anaesthesia, and coma[2]. There has been relatively little work done, however, on computational modelling of TEP waveform morphologies, and how these spatiotemporal patterns emerge from a combination of global brain network structure and local physiological characteristics. Here we present a novel connectome-based neural mass model of TEPs that accurately reproduces recordings across multiple subjects and stimulation sites. We employ a biophysical electric field model (using the simnibs[3] library) to identify the electrical field (‘E-field’) distribution over the cortical surface resulting from stimulation at a given TMS coil location and orientation, that is based on T1-weighted MRI-derived cortical geometry, and personalized to individual subjects. These TMS-induced E-field maps are then summed to yield a current injection pattern over regions in a canonical freesurfer-based brain parcellation. Whole-brain neural activity is modelled with a network of oscillatory (Fitzhugh-Nagumo) units[4,5], coupled by anatomical connectivity weights derived from diffusion-weighted MRI tractography[6], and perturbed by a brief square-wave current injection weighted regionally by the cortical E-field map magnitudes. Using this model we are able to accurately reproduce the typical radially propagating TEP patterns under a wide range of parameter values. For the later (150ms+) TEP components however, we find that it is necessary to modify the weight of cortico-thalamic and thalamo-cortical projections in the tractography-defined anatomical connectivity (see also [7]), which has the effect of promoting recurrent activity patterns. These results contribute important insights to our long-term objective of developing an accurate model of TEPs that can be used to guide the design and administration of TMS-EEG for excitability mapping in clinical contexts.


1\. Ilmoniemi, R. J. & Kicić, D. Brain Topogr. 22, 233–248 (2010).

2\. Massimini, M. et al. Science vol. 309 2228–2232 (2005).

3\. Saturnino, G. B. et al. bioRxiv 500314 (2018) doi:10.1101/500314.

4\. Izhikevich, E. M. & FitzHugh, R. Scholarpedia J. 1, 1349 (2006).

5\. Spiegler, A., et al. eNeuro 3 (2016).

6\. Schirner, M., et al. Neuroimage 117, 343–357 (2015).

7\. Bensaid, S. et al. Frontiers in Systems Neuroscience 13:59 (2019)


John Griffiths

Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health

Sunday July 19, 2020 8:00pm - 9:00pm CEST
Slot 13