Modeling transcranial magnetic stimulation from the induced electric fields to the membrane potentials along tractography-based white matter fiber tracts

Abstract

Objective. Transcranial magnetic stimulation (TMS) is a promising non-invasive tool for modulating the brain activity. Despite the widespread therapeutic and diagnostic use of TMS in neurology and psychiatry, its observed response remains hard to predict, limiting its further development and applications. Although the stimulation intensity is always maximum at the cortical surface near the coil, experiments reveal that TMS can affect deeper brain regions as well. Approach. The explanation of this spread might be found in the white matter fiber tracts, connecting cortical and subcortical structures. When applying an electric field on neurons, their membrane potential is altered. If this change is significant, more likely near the TMS coil, action potentials might be initiated and propagated along the fiber tracts towards deeper regions. In order to understand and apply TMS more effectively, it is important to capture and account for this interaction as accurately as possible. Therefore, we compute, next to the induced electric fields in the brain, the spatial distribution of the membrane potentials along the fiber tracts and its temporal dynamics. Main results. This paper introduces a computational TMS model in which electromagnetism and neurophysiology are combined. Realistic geometry and tissue anisotropy are included using magnetic resonance imaging and targeted white matter fiber tracts are traced using tractography based on diffusion tensor imaging. The position and orientation of the coil can directly be retrieved from the neuronavigation system. Incorporating these features warrants both patient-and case-specific results. Significance. The presented model gives insight in the activity propagation through the brain and can therefore explain the observed clinical responses to TMS and their inter-and/or intra-subject variability. We aspire to advance towards an accurate, flexible and personalized TMS model that helps to understand stimulation in the connected brain and to target more focused and deeper brain regions.S Online supplementary data available from stacks.iop.org/jne/13/026028/mmedia

A multi-scale computational model of the effects of TMS on motor cortex

“…This can be explained on the basis of the propagation of action potentials along white matter fiber tracts. Recent studies modeled tractography-based white matter fiber tracts using diffusion tensor imaging (DTI) and observed activation of axon bundles 9, 11, 12 . Compared to fiber tracts in previous modeling, we modeled straightly stretched axons of L5 PNs inside the WM.…”

Biophysically Realistic Neuron Models for Simulation of Cortical Stimulation

“…3D geometries of intracortical axonal arbors are particularly important for accurate coupling to E-fields. Several studies modeled artificially-defined, branched axons [7,20,21] or white-matter axons defined using tractography based on diffusion tensor imaging data [22]. Two studies modeled uniform E-field stimulation of pyramidal cells with reconstructed axon arbors, but used either passive membrane properties [9] or active membrane properties drawn from another model and flattened, 2D representation of the axons [8].…”

How to include the variability of TMS responses in simulations: a speech mapping case study

“…Indeed, the directional derivative of this E l along the neural bre describes the effect of stimulation by electromagnetic induction, such as TMS, on the membrane potential V. All tracts are assumed to be myelinated neurons, containing compartments representing passive dendrites, a passive soma, an active axon hillock, an active initial segment and alternating passive myelinated internodes and active Ranvier nodes, and are modelled with passive and active membrane properties. Their behaviour as function of time and space is computed by the following compartmental cable equation (De Geeter et al 2016):…”