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Ongoing brain rhythms shape I-wave properties in a computational model.
Brain Stimulation 2018 July
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.
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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