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Ongoing brain rhythms shape I-wave properties in a computational model
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Abstract
Background
Responses to transcranial magnetic stimulation (TMS) are notoriously variable. Previous studies have observed a dependence of TMS-induced responses on ongoing brain activity, for instance sensorimotor rhythms. This suggests an opportunity for the development of more effective stimulation protocols through closed-loop TMS-EEG. However, it is not yet clear how features of ongoing activity affect the responses of cortical circuits to TMS.
Objective/Hypothesis
Here we investigate the dependence of TMS-responses on power and phase of ongoing oscillatory activity in a computational model of TMS-induced I-waves.
Methods
The model comprises populations of cortical layer 2/3 (L2/3) neurons and a population of cortical layer 5 (L5) neurons and generates I-waves in response to TMS. Oscillatory input to the L2/3 neurons induces rhythmic fluctuations in activity of L5 neurons. TMS pulses are simulated at different phases and amplitudes of the ongoing rhythm.
Results
The model shows a robust dependence of I-wave properties on phase and power of ongoing rhythms, with the strongest response occurring for TMS at maximal L5 depolarization. The amount of phase-modulation depends on stimulation intensity, with stronger modulation for lower intensity.
Conclusion
The model predicts that responses to TMS are highly variable for low stimulation intensities if ongoing brain rhythms are not taken into account. Closed-loop TMS-EEG holds promise for obtaining more reliable TMS effects.
Title: Ongoing brain rhythms shape I-wave properties in a computational model
Description:
Abstract
Background
Responses to transcranial magnetic stimulation (TMS) are notoriously variable.
Previous studies have observed a dependence of TMS-induced responses on ongoing brain activity, for instance sensorimotor rhythms.
This suggests an opportunity for the development of more effective stimulation protocols through closed-loop TMS-EEG.
However, it is not yet clear how features of ongoing activity affect the responses of cortical circuits to TMS.
Objective/Hypothesis
Here we investigate the dependence of TMS-responses on power and phase of ongoing oscillatory activity in a computational model of TMS-induced I-waves.
Methods
The model comprises populations of cortical layer 2/3 (L2/3) neurons and a population of cortical layer 5 (L5) neurons and generates I-waves in response to TMS.
Oscillatory input to the L2/3 neurons induces rhythmic fluctuations in activity of L5 neurons.
TMS pulses are simulated at different phases and amplitudes of the ongoing rhythm.
Results
The model shows a robust dependence of I-wave properties on phase and power of ongoing rhythms, with the strongest response occurring for TMS at maximal L5 depolarization.
The amount of phase-modulation depends on stimulation intensity, with stronger modulation for lower intensity.
Conclusion
The model predicts that responses to TMS are highly variable for low stimulation intensities if ongoing brain rhythms are not taken into account.
Closed-loop TMS-EEG holds promise for obtaining more reliable TMS effects.
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