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From oscillatory transcranial current stimulation to scalp EEG changes: a biophysical and physiological modeling study.

Merlet I, Birot G, Salvador R, Molaee-Ardekani B, Mekonnen A, Soria-Frish A, Ruffini G, Miranda PC, Wendling F - PLoS ONE (2013)

Bottom Line: In order to account for tCS effects and following current biophysical models, the calculated component of the electric field normal to the cortex was used to locally influence the activity of neuronal populations.Moreover, additional information was also brought by the model at other electrode positions or stimulation frequency.This suggests that our modeling approach can be used to compare, interpret and predict changes occurring on EEG with respect to parameters used in specific stimulation configurations.

View Article: PubMed Central - PubMed

Affiliation: INSERM, Université de Rennes 1, LTSI, Rennes, France. isabelle.merlet@univ-rennes1.fr

ABSTRACT
Both biophysical and neurophysiological aspects need to be considered to assess the impact of electric fields induced by transcranial current stimulation (tCS) on the cerebral cortex and the subsequent effects occurring on scalp EEG. The objective of this work was to elaborate a global model allowing for the simulation of scalp EEG signals under tCS. In our integrated modeling approach, realistic meshes of the head tissues and of the stimulation electrodes were first built to map the generated electric field distribution on the cortical surface. Secondly, source activities at various cortical macro-regions were generated by means of a computational model of neuronal populations. The model parameters were adjusted so that populations generated an oscillating activity around 10 Hz resembling typical EEG alpha activity. In order to account for tCS effects and following current biophysical models, the calculated component of the electric field normal to the cortex was used to locally influence the activity of neuronal populations. Lastly, EEG under both spontaneous and tACS-stimulated (transcranial sinunoidal tCS from 4 to 16 Hz) brain activity was simulated at the level of scalp electrodes by solving the forward problem in the aforementioned realistic head model. Under the 10 Hz-tACS condition, a significant increase in alpha power occurred in simulated scalp EEG signals as compared to the no-stimulation condition. This increase involved most channels bilaterally, was more pronounced on posterior electrodes and was only significant for tACS frequencies from 8 to 12 Hz. The immediate effects of tACS in the model agreed with the post-tACS results previously reported in real subjects. Moreover, additional information was also brought by the model at other electrode positions or stimulation frequency. This suggests that our modeling approach can be used to compare, interpret and predict changes occurring on EEG with respect to parameters used in specific stimulation configurations.

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Simulation pipeline.3D T1 MRI images are segmented into binary masks of the different head tissues in order to get meshes of the scalp, skull and brain surface (realistic head model) as well as of the white matter (WM)/grey matter (GM) interface. Unit dipoles are located at the barycenter of the triangles of this WM/GM mesh and set perpendicular to the triangle surface. This dipole layer over the cortex defines the source space. The forward problem is computed for each dipole using the Boundary Element Method (BEM) in order to get the leadfield matrix A that represents the contribution of each unit dipole of the mesh at each of the 19 scalp electrodes considered in our simulations (orange arrows in the pipeline). In order to get a physical model of the current distribution after tCS stimulation, surface meshes representing the boundaries between the different head tissues are transformed into volume meshes. In addition, virtual tCS electrodes are also represented into the model and can be placed at any scalp location (in our simulation protocol, we used PO9-PO10 location of the international 10–10 system). The electric field is calculated using the Finite Element Method (FEM) and the normal component of the field  is mapped on the surface mesh of the WM/GM interface.  values are then averaged over 66 macro-regions to get the 66  coefficients representing the mean field effect during tCS. We used then a model of coupled neuronal populations, with parameters of each population being adjusted to generate alpha-like activity, and connectivity between populations being defined in order to account for the thalamic input.  coefficients can be added to the average membrane potential of pyramidal cells of each cortical neuronal population in order to mimic the de- or hyper-polarizing effect of the electric field and to get the resulting time-varying activities at the level of each cortical macro-region (green arrows in the pipeline). The resulting spatio-temporal source matrix S is multiplied by leadfield matrix A to get the simulated EEG data under tCS condition.
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pone-0057330-g003: Simulation pipeline.3D T1 MRI images are segmented into binary masks of the different head tissues in order to get meshes of the scalp, skull and brain surface (realistic head model) as well as of the white matter (WM)/grey matter (GM) interface. Unit dipoles are located at the barycenter of the triangles of this WM/GM mesh and set perpendicular to the triangle surface. This dipole layer over the cortex defines the source space. The forward problem is computed for each dipole using the Boundary Element Method (BEM) in order to get the leadfield matrix A that represents the contribution of each unit dipole of the mesh at each of the 19 scalp electrodes considered in our simulations (orange arrows in the pipeline). In order to get a physical model of the current distribution after tCS stimulation, surface meshes representing the boundaries between the different head tissues are transformed into volume meshes. In addition, virtual tCS electrodes are also represented into the model and can be placed at any scalp location (in our simulation protocol, we used PO9-PO10 location of the international 10–10 system). The electric field is calculated using the Finite Element Method (FEM) and the normal component of the field is mapped on the surface mesh of the WM/GM interface. values are then averaged over 66 macro-regions to get the 66 coefficients representing the mean field effect during tCS. We used then a model of coupled neuronal populations, with parameters of each population being adjusted to generate alpha-like activity, and connectivity between populations being defined in order to account for the thalamic input. coefficients can be added to the average membrane potential of pyramidal cells of each cortical neuronal population in order to mimic the de- or hyper-polarizing effect of the electric field and to get the resulting time-varying activities at the level of each cortical macro-region (green arrows in the pipeline). The resulting spatio-temporal source matrix S is multiplied by leadfield matrix A to get the simulated EEG data under tCS condition.

Mentions: In this section, we provide the reader with details about both the implementation of the model and the parameter values specifically used in this study. The proposed pipeline to simulate EEG data under tCS is summarized in Figure 3.


From oscillatory transcranial current stimulation to scalp EEG changes: a biophysical and physiological modeling study.

Merlet I, Birot G, Salvador R, Molaee-Ardekani B, Mekonnen A, Soria-Frish A, Ruffini G, Miranda PC, Wendling F - PLoS ONE (2013)

Simulation pipeline.3D T1 MRI images are segmented into binary masks of the different head tissues in order to get meshes of the scalp, skull and brain surface (realistic head model) as well as of the white matter (WM)/grey matter (GM) interface. Unit dipoles are located at the barycenter of the triangles of this WM/GM mesh and set perpendicular to the triangle surface. This dipole layer over the cortex defines the source space. The forward problem is computed for each dipole using the Boundary Element Method (BEM) in order to get the leadfield matrix A that represents the contribution of each unit dipole of the mesh at each of the 19 scalp electrodes considered in our simulations (orange arrows in the pipeline). In order to get a physical model of the current distribution after tCS stimulation, surface meshes representing the boundaries between the different head tissues are transformed into volume meshes. In addition, virtual tCS electrodes are also represented into the model and can be placed at any scalp location (in our simulation protocol, we used PO9-PO10 location of the international 10–10 system). The electric field is calculated using the Finite Element Method (FEM) and the normal component of the field  is mapped on the surface mesh of the WM/GM interface.  values are then averaged over 66 macro-regions to get the 66  coefficients representing the mean field effect during tCS. We used then a model of coupled neuronal populations, with parameters of each population being adjusted to generate alpha-like activity, and connectivity between populations being defined in order to account for the thalamic input.  coefficients can be added to the average membrane potential of pyramidal cells of each cortical neuronal population in order to mimic the de- or hyper-polarizing effect of the electric field and to get the resulting time-varying activities at the level of each cortical macro-region (green arrows in the pipeline). The resulting spatio-temporal source matrix S is multiplied by leadfield matrix A to get the simulated EEG data under tCS condition.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3585369&req=5

pone-0057330-g003: Simulation pipeline.3D T1 MRI images are segmented into binary masks of the different head tissues in order to get meshes of the scalp, skull and brain surface (realistic head model) as well as of the white matter (WM)/grey matter (GM) interface. Unit dipoles are located at the barycenter of the triangles of this WM/GM mesh and set perpendicular to the triangle surface. This dipole layer over the cortex defines the source space. The forward problem is computed for each dipole using the Boundary Element Method (BEM) in order to get the leadfield matrix A that represents the contribution of each unit dipole of the mesh at each of the 19 scalp electrodes considered in our simulations (orange arrows in the pipeline). In order to get a physical model of the current distribution after tCS stimulation, surface meshes representing the boundaries between the different head tissues are transformed into volume meshes. In addition, virtual tCS electrodes are also represented into the model and can be placed at any scalp location (in our simulation protocol, we used PO9-PO10 location of the international 10–10 system). The electric field is calculated using the Finite Element Method (FEM) and the normal component of the field is mapped on the surface mesh of the WM/GM interface. values are then averaged over 66 macro-regions to get the 66 coefficients representing the mean field effect during tCS. We used then a model of coupled neuronal populations, with parameters of each population being adjusted to generate alpha-like activity, and connectivity between populations being defined in order to account for the thalamic input. coefficients can be added to the average membrane potential of pyramidal cells of each cortical neuronal population in order to mimic the de- or hyper-polarizing effect of the electric field and to get the resulting time-varying activities at the level of each cortical macro-region (green arrows in the pipeline). The resulting spatio-temporal source matrix S is multiplied by leadfield matrix A to get the simulated EEG data under tCS condition.
Mentions: In this section, we provide the reader with details about both the implementation of the model and the parameter values specifically used in this study. The proposed pipeline to simulate EEG data under tCS is summarized in Figure 3.

Bottom Line: In order to account for tCS effects and following current biophysical models, the calculated component of the electric field normal to the cortex was used to locally influence the activity of neuronal populations.Moreover, additional information was also brought by the model at other electrode positions or stimulation frequency.This suggests that our modeling approach can be used to compare, interpret and predict changes occurring on EEG with respect to parameters used in specific stimulation configurations.

View Article: PubMed Central - PubMed

Affiliation: INSERM, Université de Rennes 1, LTSI, Rennes, France. isabelle.merlet@univ-rennes1.fr

ABSTRACT
Both biophysical and neurophysiological aspects need to be considered to assess the impact of electric fields induced by transcranial current stimulation (tCS) on the cerebral cortex and the subsequent effects occurring on scalp EEG. The objective of this work was to elaborate a global model allowing for the simulation of scalp EEG signals under tCS. In our integrated modeling approach, realistic meshes of the head tissues and of the stimulation electrodes were first built to map the generated electric field distribution on the cortical surface. Secondly, source activities at various cortical macro-regions were generated by means of a computational model of neuronal populations. The model parameters were adjusted so that populations generated an oscillating activity around 10 Hz resembling typical EEG alpha activity. In order to account for tCS effects and following current biophysical models, the calculated component of the electric field normal to the cortex was used to locally influence the activity of neuronal populations. Lastly, EEG under both spontaneous and tACS-stimulated (transcranial sinunoidal tCS from 4 to 16 Hz) brain activity was simulated at the level of scalp electrodes by solving the forward problem in the aforementioned realistic head model. Under the 10 Hz-tACS condition, a significant increase in alpha power occurred in simulated scalp EEG signals as compared to the no-stimulation condition. This increase involved most channels bilaterally, was more pronounced on posterior electrodes and was only significant for tACS frequencies from 8 to 12 Hz. The immediate effects of tACS in the model agreed with the post-tACS results previously reported in real subjects. Moreover, additional information was also brought by the model at other electrode positions or stimulation frequency. This suggests that our modeling approach can be used to compare, interpret and predict changes occurring on EEG with respect to parameters used in specific stimulation configurations.

Show MeSH
Related in: MedlinePlus