Principal Component Regression on Motor Evoked Potential in Single-Pulse Transcranial Magnetic Stimulation

Motor evoked potentials (MEPs) induced by transcranial magnetic stimulation (TMS) are commonly characterized only by their onset (latency) and size (amplitude) whereas other potentially important information in the MEPs is discarded. Hence, our aim was to examine the morphological information of MEPs using principal component regression (PCR) providing additional perception of MEPs. MEPs were recorded from the first dorsal interosseous muscle following navigated TMS focused at the primary motor cortex. The PCR holding of at least 96% of total variance of the MEP dataset was performed to parameterize MEPs into principal components (PCs), which were used with non-linear least square estimation to reconstruct original MEPs. The comparison between the original and reconstructed MEPs showed that PCs, which accounted for 96% of total variance, were able to characterize the MEP morphology, i.e., the PCR summarizes the repeated information in the MEP dataset into the PC set. In addition, PCR benefited the automated quantification of MEP features as it removed the random noise caused by the environmental interference and the inconsistency of neuronal pathways. Furthermore, we could determine the minimum number of trials required to reliably represent the whole dataset by estimating the partial information of those trials accounted for. Our results showed that this partial information exponentially increased with respect to the number of trials, and saturated within 20 MEPs holding approximately 90% of total variance of the dataset.