Cortical representation of auricular muscles in humans: A robot-controlled TMS mapping and fMRI study.

BACKGROUND: Most humans have the ability to activate the auricular muscles. Although (intentional) control suggests an involvement of higher cortical centers underlying posterior auricular muscle (PAM) activation, the cortical representation of the auricular muscles is still unknown. METHODS: With the purpose of identifying a possible cortical representation area we performed automated robotic and image-guided transcranial magnetic stimulation (TMS) mapping (n = 8) and functional magnetic resonance imaging (fMRI) (n = 13). For topographical comparison, a similar experimental protocol was applied for the first dorsal interosseus muscle (FDI) of the hand. RESULTS: The calculated centers of gravity (COGs) of both muscles were located on the precentral gyrus with the PAM COGs located more laterally compared to the FDI. The distance between the mean PAM and mean FDI COG was 26.3 mm. The TMS mapping results were confirmed by fMRI, which showed a dominance of cortical activation within the precentral gyrus during the corresponding motor tasks. The correspondence of TMS and fMRI results was high. CONCLUSION: The involvement of the primary motor cortex in PAM activation might point to an evolved function of the auricular muscles in humans and/or the ability of intentional (and selective) muscle activation.

Automated TMS hotspot-hunting using a closed loop threshold-based algorithm.

BACKGROUND: Although neuronavigation is increasingly used for optimizing coil positioning, the inter-session reliability of hotspot location remains unsatisfactory, probably due to the variability of motor evoked potentials (MEPs) and residual investigator bias. PURPOSE: To increase the reliability and accuracy of hotspot location we introduce a novel automated hotspot-hunting procedure (AHH). METHODS: AHH is based on resting motor thresholds (RMTs) instead of MEP amplitudes. By combining robotic coil positioning with a closed loop target search algorithm AHH runs independently from the investigator. AHH first identifies all targets with an RMT below a defined intensity of stimulator output (MEP-positive) and then locates the motor hotspot of a target muscle by measuring RMTs at all identified MEP-positive targets. Results were compared to robotic MEP amplitude TMS mapping (MAM) using a 7×7 predefined target grid and suprathreshold intensities and manual hotspot search (MHS). Sequence of stimulation was randomized from pulse to pulse in AHH and MAM. Each procedure was tested in 8 subjects. RESULTS: Inter-session CoG shift was significantly reduced with AHH (1.4mm (SEM: 0.4)) as compared to MAM (7.0mm (SEM: 1.8)) (p=0.018) and MHS (9.6mm (SEM: 2.2)) (p=0.007). No statistical difference was observed between MAM and MHS. RMTs were reliable between sessions. CONCLUSION: Our method represents the first fully automated, i.e. investigator-independent, TMS hotspot-hunting procedure. Measuring RMTs instead of MEP amplitudes leads to significantly increased accuracy and reliability of CoG locations. Moreover, by assessing thresholds AHH is the first procedure to fulfill the original hotspot definition.