Using EEG to Explore How rTMS Produces Its Effects on Behavior

Abstract

A commonly held view is that, when delivered during the performance of a task, repetitive TMS (rTMS) influences behavior by producing transient “virtual lesions” in targeted tissue. However, findings of rTMS-related improvements in performance are difficult to reconcile with this assumption. With regard to the mechanism whereby rTMS influences concurrent task performance, a combined rTMS/EEG study conducted in our lab has revealed a complex set of relations between rTMS, EEG activity, and behavioral performance, with the effects of rTMS on power in the alpha band and on alpha:gamma phase synchrony each predicting its effect on behavior. These findings suggest that rTMS influences performance by biasing endogenous task-related oscillatory dynamics, rather than creating a “virtual lesion”. To further differentiate these two alternatives, in the present study we compared the effects of 10 Hz rTMS on neural activity with the results of an experiment in which rTMS was replaced with 10 Hz luminance flicker. We reasoned that 10 Hz flicker would produce widespread entrainment of neural activity to the flicker frequency, and comparison of these EEG results with those from the rTMS study would shed light on whether the latter also reflected entrainment to an exogenous stimulus. Results revealed pronounced evidence for “entrainment noise” produced by 10 Hz flicker—increased oscillatory power and inter-trial coherence (ITC) at the driving frequency, and increased alpha:gamma phase synchronization—that were nonetheless largely uncorrelated with behavior. This contrasts markedly with 10-Hz rTMS, for which the only evidence for stimulation-induced noise, elevated ITC at 30 Hz, differed qualitatively from the flicker results. Simultaneous recording of the EEG thus offers an important means of directly testing assumptions about how rTMS exerts its effects on behavior.

Appendix

To facilitate comparison of the present results with the results of Hamidi et al. (2009), we include in the following section relevant details of the experimental methods used in that study and in Hamidi et al. (in press).

rTMS

rTMS was applied to two different brain areas: the superior parietal lobule (SPL) and the postcentral gyrus (PCG). The area representing the leg in the somatosensory cortex of the PCG served as a control region for the behavioral study. Both areas were identified based on individual anatomy from whole-brain anatomical MRIs that were obtained for each subject prior to the study (GE Signa VH/I, 256 sagittal slices, 0.5 mm × 0.5 mm × 0.8 mm). An infrared-based frameless stereotaxy system was used to accurately target each brain area with the TMS coil (eXimia NBS, Nexstim, Helsinki, Finland). For all subjects rTMS was applied to the left hemisphere.

In rTMS_present trials, a 10-Hz rTMS train (110% of resting motor threshold) was applied during the entire 3-s delay period (30 pulses). For each brain area targeted (and, hence, for each session), a total of 2,880 pulses were delivered. TMS was delivered with a Magstim Standard Rapid magnetic stimulator fit with a 70-mm figure-8 stimulating coil (Magstim, Whitland, UK). Because rTMS_present and rTMS_absent trials were randomly intermixed, the intertrain interval varied. A minimum of 17.1 s separated each train. White noise was played in the background during all trials.

EEG Recording

EEG was recorded with a 60-channel carbon cap and TMS-compatible amplifier (Nexstim, Helsinki, Finland). This amplifier is designed to avoid saturation by the TMS pulse by employing a “sample-and-hold” circuit that keeps the output of the amplifier constant from 100 μs pre- to 2-ms post-stimulus (Virtanen et al. 1999). To further reduce residual TMS-related artifacts, the impedance at each electrode was kept below 3 kΩ (Massimini et al. 2005). The right mastoid served as the reference during recording, and the ERP waveforms were algebraically rereferenced to the average of all 60 electrodes offline. Additionally, eye movements were recorded using two electrodes placed near the eyes. Data were digitized at a rate of 1450 Hz with a 16-bit resolution.

Data Preprocessing

Data were processed offline using the EEGlab toolbox (v. 6.03, Delorme and Makeig 2004) running in a MATLAB environment (Mathworks, Natick, MA). The data were first downsampled to 500 Hz (after application of a low-pass anti-aliasing filter) and then band-pass filtered between 0.1 and 500 Hz. The data were then cleaned of large movement-related artifacts and channels with excessive noise were reinterpolated using spherical spline interpolation, as in the present study.

TMS Artifact Removal with ICA

Although the Nexstim EEG amplifier described above was specifically designed to minimize the electrical artifacts produced by TMS, in some cases there were nonetheless remaining artifacts in the EEG data. The use of ICA to remove this artifact is the primary focus of the Hamidi et al. (in press) paper. In the context of the present paper, this raises some concern that, because TMS was delivered at 10 Hz, evidence of entrainment to the stimulation frequency may have been removed along with TMS-related artifacts. Although there is no way to be sure that some physiological data was not removed along with the 10-Hz TMS artifact, there are several reasons to believe that any removal that did occur was minimal. First, electrical signals originating from the brain become smeared out as they pass through the skull. As a result, physiological EEG signals, even if from a very localized source, are typically observable at many electrodes on the scalp. TMS-related artifacts, on the other hand, originate from outside the skull and, with TMS-compatible EEG systems, localize to only the few electrodes surrounding the TMS coil (Komssi et al. 2004). TMS-related artifacts are also temporally predictable: reliably occurring with the delivery of each pulse and typically lasting only a few milliseconds (Kahkonen et al. 2005; Virtanen et al. 1999). Thus, ICA, a method that separates statistically independent sources from a mixed signal, is ideally suited to separate TMS-related electrical artifacts from physiological data obtained via EEG recording. One qualification of these general points should be noted, however. As discussed below, ICA components may in some cases contain both physiological and artifactual signals. In these cases, the artifact is likely to be much stronger than any, potentially entrainment-related, overlapping physiological signals. Therefore, if one finds a component having the strongest loadings at only a few electrodes, this could present a scaling problem where the much weaker contributions of the entrained physiological signal, which may also be more spatially blurred than the artifact, is no longer visible.

Two rounds of ICA were performed on the data to assure that less prominent TMS artifacts were detected. The first ICA was performed on continuous EEG data, and the second was performed solely on data for epochs where TMS was applied. TMS artifact components were identified using three characteristics (examples of each characteristic can be seen in Fig. 1 of Hamidi et al. in press). First, as described above, the artifact should be relatively spatially localized. Second, the power spectrum of an ICA component should have a strong peak at 10 Hz (accompanied by peaks at every harmonic of 10 Hz), because TMS was delivered at 10 Hz. Although physiological signals that do not reflect entrainment to an exogenous source may also have a peak around 10 Hz (the center of the α-band), this peak typically covers a wider frequency range, and does not exhibit strong harmonics. Additionally, physiological signals show a general 1/frequency pattern in the power spectrum, whereas TMS-related artifacts are superimposed on a flat power spectrum. A third criterion concerned the timecourse of the component activity. With the TMS compatible EEG system, the artifact, if present, is limited to the first 10–15 ms after the pulse (Ilmoniemi et al. 1997; Virtanen et al. 1999). Because the timing of the TMS train was known, an ICA component reflecting the TMS artifact had to peak within a few milliseconds of the start of each TMS pulse in the train.

Although in most instances it was straightforward to differentiate TMS artifacts from physiological data, following the guidelines outlined above, for several subjects ICA also produced one or more components that seemed to contain elements of both neurophysiological data and TMS artifact. In these cases, the component was kept for further analysis, with the idea that a second round of ICA might separate the two. If mixed TMS and physiological components were identified following the second ICA, they were then discarded. Although the potential removal of physiologically relevant data is a legitimate cause for concern, it should be noted that relatively few components (1.6(±1.4) per subject) that included both TMS artifact and physiological activity were removed.