Yu, G. J. | Search bioRxiv

bioRxiv2026

Computational investigation of circuit mechanisms underlying short-latency responses to cortical stimulation

Transcranial magnetic stimulation (TMS) over the primary motor cortex (M1) elicits a series of high frequency volleys termed D- and I-waves measured epidurally in the corticospinal tract of awake humans. Further, intracortical microstimulation (ICMS) in M1 of non-human primates evokes D- and I-wave responses similar to those observed in TMS. The cortical circuits and mechanisms involved in the generation of D- and I-waves by stimulation of M1 remain unclear. Here, we implemented computational models of cortical columns with laminarly-organized biophysically-based neurons, following existing models published in the literature: (1) M1 - single compartment (SC), (2) M1 - multi-compartment (MC), (3) primary auditory cortex (A1) - MC, and (4) primary somatosensory cortex (S1) - MC. The network connectivity of each model represented wiring found in the respective cortical regions. The direct effects of stimulation-induced electric fields were modeled as activation of different proportions of pyramidal neurons (PNs) across layers, and dose response curves were constructed for layer 5 (L5) PNs. Both the M1 and A1 models reproduced D- and I-waves, with the magnitude of I-waves increasing with higher recruitment of layer 2/3 and layer 5 PNs. The S1-MC model evoked rhythmic firing activity but with timings mismatched to experimental I-waves. The models replicated the experimentally observed effects of pharmacological agents on I-waves, and virtual lesions of specific neural populations across models revealed plausible microcircuit explanations for the first and later I-waves. This comprehensive comparison of models across multiple cortical regions identified consistent mechanisms underlying the cortical response to TMS and contributes to the refinement of computational strategies for optimizing stimulation paradigms.

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bioRxiv2024

Circuits and mechanisms for TMS-induced corticospinal waves: Connecting sensitivityanalysis to the network graph

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. AUTHOR SUMMARYUnderstanding circuit mechanisms underlying the response to transcranial magnetic stimulation remains a significant challenge for translational and clinical research. Computational models can reconstruct network activity in response to stimulation, but basic sensitivity analyses are insufficient to identify the fundamental circuit properties that underly an evoked response. We developed a data-driven neuronal network model of motor cortex, constrained with human recordings, that reproduced the corticospinal response to magnetic stimulation. The model supported several hypotheses, e.g., the importance of stimulating incoming fibers as well as neurons within the cortical column and the relevance of both excitatory and inhibitory neurons. Following a sensitivity analysis, we conducted a secondary structural analysis that linked the results of the sensitivity analysis to the network using machine learning. The structural analysis pointed to anatomical mechanisms that contributed to specific peaks in the response. Generally, given the anatomy and circuit of a neural region, identifying strongly connected paths in the network and the conduction delays of these paths can screen for important contributors to response peaks. This work supports and expands on hypotheses explaining the response to transcranial magnetic stimulation and adds a novel method for identifying generalizable neural circuit mechanisms.

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