Neuron-Level Motor Cortex Mapping with Transcranial Magnetic Stimulation
Poster No:
2122
Submission Type:
Abstract Submission
Authors:
Ying Jing1, Ole Numssen1, Gesa Hartwigsen2,3, Thomas Knösche1, Konstantin Weise1,4
Institutions:
1Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, 2Leipzig University, Leipzig, Saxony, 3Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 4Institute of Electrical Energy Technology, Leipzig University of Applied Sciences, Leipzig, Germany
First Author:
Co-Author(s):
Gesa Hartwigsen
Leipzig University|Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Saxony|Leipzig, Germany
Leipzig University|Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Saxony|Leipzig, Germany
Konstantin Weise
Max Planck Institute for Human Cognitive and Brain Sciences|Institute of Electrical Energy Technology, Leipzig University of Applied Sciences
Leipzig, Saxony|Leipzig, Germany
Max Planck Institute for Human Cognitive and Brain Sciences|Institute of Electrical Energy Technology, Leipzig University of Applied Sciences
Leipzig, Saxony|Leipzig, Germany
Introduction:
Transcranial magnetic stimulation (TMS) is a non-invasive technique that modulates brain activities by generating a time-varying electric field in the brain, capable of eliciting action potentials in cortical neurons [1]. By using biophysical modeling of the induced electric field (E-field) the cortical structures that are effectively stimulated by TMS can be identified [2-4]. We recently proposed a regression-based method to perform structure-function mappings by analyzing the relationship between behavioral modulation and local cortical stimulation strength [5-7].
Physiological properties of neurons, especially the orientation of the neurons with respect to the E-field, determine the firing threshold in response to TMS [8]. However, existing TMS modeling works neglect the diversity of neuron types and geometries across the different cortical layers. To bridge this gap, our study incorporates a recently published average response model of cortical neuron [8] into the regression-based TMS mapping of the motor cortex.
Physiological properties of neurons, especially the orientation of the neurons with respect to the E-field, determine the firing threshold in response to TMS [8]. However, existing TMS modeling works neglect the diversity of neuron types and geometries across the different cortical layers. To bridge this gap, our study incorporates a recently published average response model of cortical neuron [8] into the regression-based TMS mapping of the motor cortex.
Methods:
Fourteen healthy, right-handed participants (seven females, aged 21-38 years) were recruited [5]. High-resolution realistic head models were constructed from individual T1, T2, and diffusion weighted images acquired on a 3T Magnetic resonance imaging (MRI) scanner. Subsequently, the cortical layers were added within the primary motor cortex region of interest (ROI). A total of 900–1100 single TMS pulses were applied around the left motor hotspot with 150% motor threshold (MT) [5]. Coil positions and angles were randomly selected for each stimulation to sample electric field distributions and corresponding motor-evoked potentials (MEP) from the first dorsal interosseous (FDI) muscle. E-field simulations were performed for each pulse using SimNIBS v4.0 [9, 10]. Neuron firing thresholds for every element in the motor cortex were determined by computing the polar angle (θ) and the percentage decay of the electric field magnitude (∆|E|) on layer 2/3 and layer 5 using the average threshold model from Weise et al. [8].
Subsequently, we calculated the effective E-field (E_eff) the neurons are responsive to by dividing the E-field magnitude (E_mag) by the normalized firing threshold (thresh) for each ROI element. These effective E-fields were then nonlinearly regressed with the elicited MEPs to locate their cortical origin on the cortical layer level. The highest goodness-of-fit (R2) identifies the cortical site housing the relevant neuronal populations. We compared current mapping results obtained from the E_eff (neuron-enhanced model) with those derived solely from the E_mag (magnitude model), and the cortical column cosine model, which counts the normal component of the E-field.
Subsequently, we calculated the effective E-field (E_eff) the neurons are responsive to by dividing the E-field magnitude (E_mag) by the normalized firing threshold (thresh) for each ROI element. These effective E-fields were then nonlinearly regressed with the elicited MEPs to locate their cortical origin on the cortical layer level. The highest goodness-of-fit (R2) identifies the cortical site housing the relevant neuronal populations. We compared current mapping results obtained from the E_eff (neuron-enhanced model) with those derived solely from the E_mag (magnitude model), and the cortical column cosine model, which counts the normal component of the E-field.
Results:
Localization results (depicted as R2 maps in Fig. 1) revealed a consistent pattern when comparing the magnitude model with the neuron-enhanced model, which incorporated information from layer 2/3 and layer 5. The locations of elements with maximum R2 did not show distinguishable differences across models. However, layer 5 exhibited significantly higher R2 values compared to the standard method (Z = 10, p = 0.013*, Wilcoxon test), suggesting a more precise functional localization of the cortical origin of the elicited MEPs. No significant difference was observed between the magnitude model and the layer 2/3 (Z = 40, p = 0.463, Wilcoxon test), or the cortical column cosine model (Z = 19, p = 0.064, Wilcoxon test).
·Figure 1. R2 maps
Conclusions:
The current study advanced TMS modeling by incorporating neuron-specific factors, adjusting the E-field magnitude with the normalized firing threshold at the individual neuron level. The observed improvements in localization results in layer 5 emphasize the potential source of observing MEPs originating from this layer in the motor cortex. This neuron-enhanced model lays the groundwork for more comprehensive and accurate interpretations of TMS-induced effects in future works.
Brain Stimulation:
Non-invasive Magnetic/TMS 2
Modeling and Analysis Methods:
Other Methods
Motor Behavior:
Motor Behavior Other
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 1
Keywords:
Modeling
Motor
Neuron
Transcranial Magnetic Stimulation (TMS)
Other - Electrical field simulation
1|2Indicates the priority used for review
Provide references using author date format
[2] Bungert. (2017), 'Where does TMS stimulate the motor cortex? Combining electrophysiological measurements and realistic field estimates to reveal the affected cortex position', Cerebral Cortex, 27: 5083-94.
[3] Hartwigsen. (2015), 'Modeling the effects of noninvasive transcranial brain stimulation at the biophysical, network, and cognitive level', Progress in brain research, 222: 261-87.
[4] Weise. (2020), 'A novel approach to localize cortical TMS effects', Neuroimage, 209: 116486.
[5] Numssen. (2021), 'Efficient high-resolution TMS mapping of the human motor cortex by nonlinear regression', Neuroimage, 245: 118654.
[6] Weise. (2023), 'Precise motor mapping with transcranial magnetic stimulation', Nature protocols, 18: 293-318.
[7] Jing. (2023), 'Modeling the effects of transcranial magnetic stimulation on spatial attention', Physics in Medicine & Biology, 68: 214001.
[8] Weise. (2023), 'Directional sensitivity of cortical neurons towards TMS induced electric fields', Imaging Neuroscience.
[9] Saturnino. (2019), 'Electric field simulations for transcranial brain stimulation using FEM: an efficient implementation and error analysis', Cerebral Cortex Journal of neural engineering, 16: 066032.
[10] Thielscher. (2015), "Field modeling for transcranial magnetic stimulation: a useful tool to understand the physiological effects of TMS?" In 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC), 222-25. IEEE.