Yu Gene J, Ranieri Federico, Di Lazzaro Vincenzo, Sommer Marc A, Peterchev Angel V, Grill Warren M
Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America.
Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, United States of America.
PLoS Comput Biol. 2024 Dec 5;20(12):e1012640. doi: 10.1371/journal.pcbi.1012640. eCollection 2024 Dec.
Transcranial magnetic stimulation (TMS) is a non-invasive, FDA-cleared treatment for neuropsychiatric disorders with broad potential for new applications, but the neural circuits that are engaged during TMS are still poorly understood. Recordings of neural activity from the corticospinal tract provide a direct readout of the response of motor cortex to TMS, and therefore a new opportunity to model neural circuit dynamics. The study goal was to use epidural recordings from the cervical spine of human subjects to develop a computational model of a motor cortical macrocolumn through which the mechanisms underlying the response to TMS, including direct and indirect waves, could be investigated. An in-depth sensitivity analysis was conducted to identify important pathways, and machine learning was used to identify common circuit features among these pathways. Sensitivity analysis identified neuron types that preferentially contributed to single corticospinal waves. Single wave preference could be predicted using the average connection probability of all possible paths between the activated neuron type and L5 pyramidal tract neurons (PTNs). For these activations, the total conduction delay of the shortest path to L5 PTNs determined the latency of the corticospinal wave. Finally, there were multiple neuron type activations that could preferentially modulate a particular corticospinal wave. The results support the hypothesis that different pathways of circuit activation contribute to different corticospinal waves with participation of both excitatory and inhibitory neurons. Moreover, activation of both afferents to the motor cortex as well as specific neuron types within the motor cortex initiated different I-waves, and the results were interpreted to propose the cortical origins of afferents that may give rise to certain I-waves. The methodology provides a workflow for performing computationally tractable sensitivity analyses on complex models and relating the results to the network structure to both identify and understand mechanisms underlying the response to acute stimulation.
经颅磁刺激（TMS）是一种经美国食品药品监督管理局（FDA）批准的非侵入性治疗方法，可用于治疗神经精神疾病，具有广泛的新应用潜力，但TMS过程中涉及的神经回路仍未得到充分了解。来自皮质脊髓束的神经活动记录提供了运动皮层对TMS反应的直接读数，因此为模拟神经回路动力学提供了新机会。该研究的目标是利用人体受试者颈椎的硬膜外记录来开发一个运动皮质大柱的计算模型，通过该模型可以研究对TMS反应的潜在机制，包括直接波和间接波。进行了深入的敏感性分析以确定重要通路，并使用机器学习来识别这些通路中的共同回路特征。敏感性分析确定了对单个皮质脊髓波有优先贡献的神经元类型。可以使用激活的神经元类型与L5锥体束神经元（PTN）之间所有可能路径的平均连接概率来预测单波偏好。对于这些激活，到L5 PTN的最短路径的总传导延迟决定了皮质脊髓波的潜伏期。最后，有多种神经元类型激活可以优先调节特定的皮质脊髓波。结果支持这样的假设，即不同的回路激活途径在兴奋性和抑制性神经元的参与下对不同的皮质脊髓波有贡献。此外，运动皮层传入神经以及运动皮层内特定神经元类型的激活引发了不同的I波，研究结果被解释为提出了可能产生某些I波的传入神经的皮质起源。该方法提供了一个工作流程，用于对复杂模型进行计算上易于处理的敏感性分析，并将结果与网络结构相关联，以识别和理解对急性刺激反应背后的机制。
