Aman S Aberra1, Boshuo Wang2, Warren M Grill3, Angel V Peterchev4. 1. Department of Biomedical Engineering, Duke University, Durham, NC, 27708, USA. Electronic address: aman.aberra@duke.edu. 2. Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, 27710, USA. Electronic address: boshuo.wang@duke.edu. 3. Department of Biomedical Engineering, Duke University, Durham, NC, 27708, USA; Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27708, USA; Department of Neurobiology, School of Medicine, Duke University, Durham, NC, 27710, USA; Department of Neurosurgery, School of Medicine, Duke University, Durham, NC, 27710, USA. Electronic address: warren.grill@duke.edu. 4. Department of Biomedical Engineering, Duke University, Durham, NC, 27708, USA; Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, 27710, USA; Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27708, USA; Department of Neurosurgery, School of Medicine, Duke University, Durham, NC, 27710, USA. Electronic address: angel.peterchev@duke.edu.
Abstract
BACKGROUND: Transcranial magnetic stimulation (TMS) enables non-invasive modulation of brain activity with both clinical and research applications, but fundamental questions remain about the neural types and elements TMS activates and how stimulation parameters affect the neural response. OBJECTIVE: To develop a multi-scale computational model to quantify the effect of TMS parameters on the direct response of individual neurons. METHODS: We integrated morphologically-realistic neuronal models with TMS-induced electric fields computed in a finite element model of a human head to quantify the cortical response to TMS with several combinations of pulse waveforms and current directions. RESULTS: TMS activated with lowest intensity intracortical axonal terminations in the superficial gyral crown and lip regions. Layer 5 pyramidal cells had the lowest thresholds, but layer 2/3 pyramidal cells and inhibitory basket cells were also activated at most intensities. Direct activation of layers 1 and 6 was unlikely. Neural activation was largely driven by the field magnitude, rather than the field component normal to the cortical surface. Varying the induced current direction caused a waveform-dependent shift in the activation site and provided a potential mechanism for experimentally observed differences in thresholds and latencies of muscle responses. CONCLUSIONS: This biophysically-based simulation provides a novel method to elucidate mechanisms and inform parameter selection of TMS and other cortical stimulation modalities. It also serves as a foundation for more detailed network models of the response to TMS, which may include endogenous activity, synaptic connectivity, inputs from intrinsic and extrinsic axonal projections, and corticofugal axons in white matter.
BACKGROUND: Transcranial magnetic stimulation (TMS) enables non-invasive modulation of brain activity with both clinical and research applications, but fundamental questions remain about the neural types and elements TMS activates and how stimulation parameters affect the neural response. OBJECTIVE: To develop a multi-scale computational model to quantify the effect of TMS parameters on the direct response of individual neurons. METHODS: We integrated morphologically-realistic neuronal models with TMS-induced electric fields computed in a finite element model of a human head to quantify the cortical response to TMS with several combinations of pulse waveforms and current directions. RESULTS: TMS activated with lowest intensity intracortical axonal terminations in the superficial gyral crown and lip regions. Layer 5 pyramidal cells had the lowest thresholds, but layer 2/3 pyramidal cells and inhibitory basket cells were also activated at most intensities. Direct activation of layers 1 and 6 was unlikely. Neural activation was largely driven by the field magnitude, rather than the field component normal to the cortical surface. Varying the induced current direction caused a waveform-dependent shift in the activation site and provided a potential mechanism for experimentally observed differences in thresholds and latencies of muscle responses. CONCLUSIONS: This biophysically-based simulation provides a novel method to elucidate mechanisms and inform parameter selection of TMS and other cortical stimulation modalities. It also serves as a foundation for more detailed network models of the response to TMS, which may include endogenous activity, synaptic connectivity, inputs from intrinsic and extrinsic axonal projections, and corticofugal axons in white matter.
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