Geeter Nele De, Dupré Luc, Crevecoeur Guillaume
Department of Electrical Energy, Systems and Automation, Ghent University, Technologiepark 913, B-9052 Zwijnaarde, Belgium.
J Neural Eng. 2016 Apr;13(2):026028. doi: 10.1088/1741-2560/13/2/026028. Epub 2016 Mar 2.
Transcranial magnetic stimulation (TMS) is a promising non-invasive tool for modulating the brain activity. Despite the widespread therapeutic and diagnostic use of TMS in neurology and psychiatry, its observed response remains hard to predict, limiting its further development and applications. Although the stimulation intensity is always maximum at the cortical surface near the coil, experiments reveal that TMS can affect deeper brain regions as well.
The explanation of this spread might be found in the white matter fiber tracts, connecting cortical and subcortical structures. When applying an electric field on neurons, their membrane potential is altered. If this change is significant, more likely near the TMS coil, action potentials might be initiated and propagated along the fiber tracts towards deeper regions. In order to understand and apply TMS more effectively, it is important to capture and account for this interaction as accurately as possible. Therefore, we compute, next to the induced electric fields in the brain, the spatial distribution of the membrane potentials along the fiber tracts and its temporal dynamics.
This paper introduces a computational TMS model in which electromagnetism and neurophysiology are combined. Realistic geometry and tissue anisotropy are included using magnetic resonance imaging and targeted white matter fiber tracts are traced using tractography based on diffusion tensor imaging. The position and orientation of the coil can directly be retrieved from the neuronavigation system. Incorporating these features warrants both patient- and case-specific results.
The presented model gives insight in the activity propagation through the brain and can therefore explain the observed clinical responses to TMS and their inter- and/or intra-subject variability. We aspire to advance towards an accurate, flexible and personalized TMS model that helps to understand stimulation in the connected brain and to target more focused and deeper brain regions.
经颅磁刺激（TMS）是一种很有前景的用于调节大脑活动的非侵入性工具。尽管TMS在神经病学和精神病学领域有广泛的治疗和诊断应用，但其观察到的反应仍然难以预测，这限制了其进一步的发展和应用。虽然在靠近线圈的皮质表面刺激强度始终最大，但实验表明TMS也能影响更深的脑区。
这种传播现象的解释可能在于连接皮质和皮质下结构的白质纤维束。当对神经元施加电场时，它们的膜电位会发生改变。如果这种变化很显著，更有可能在TMS线圈附近，动作电位可能会被引发并沿着纤维束向更深的区域传播。为了更有效地理解和应用TMS，尽可能准确地捕捉和考虑这种相互作用非常重要。因此，除了计算大脑中的感应电场外，我们还计算沿纤维束的膜电位的空间分布及其时间动态。
本文介绍了一种将电磁学和神经生理学相结合的计算TMS模型。利用磁共振成像纳入了真实的几何形状和组织各向异性，并基于扩散张量成像使用纤维束成像追踪了目标白质纤维束。线圈的位置和方向可以直接从神经导航系统中获取。纳入这些特征保证了针对患者和具体病例的结果。
所提出的模型能够深入了解大脑中的活动传播，因此可以解释观察到的对TMS的临床反应及其个体间和/或个体内的变异性。我们希望朝着一个准确、灵活且个性化的TMS模型迈进，该模型有助于理解连通大脑中的刺激，并针对更聚焦和更深的脑区。
