Characterizing and minimizing the contribution of sensory inputs to TMS-evoked potentials

Abstract

Background: Transcranial magnetic stimulation (TMS) evokes voltage deflections in electroencephalographic (EEG) recordings, known as TMS-evoked potentials (TEPs), which are increasingly used to study brain dynamics. However, the extent to which TEPs reflect activity directly evoked by magnetic rather than sensory stimulation is unclear.Objective: To characterize and minimize the contribution of sensory inputs to TEPs.Methods: Twenty-four healthy participants received TMS over the motor cortex using two different intensities (below and above cortical motor threshold) and waveforms (monophasic, biphasic). TMS was also applied over the shoulder as a multisensory control condition.Common sensory attenuation measures, including coil padding and noise masking, were adopted. We examined spatiotemporal relationships between the EEG responses to the scalp and shoulder stimulations at sensor and source levels. Furthermore, we compared three different filters (independent component analysis, signal-space projection with source informed reconstruction (SSP-SIR) and linear regression) designed to attenuate the impact of sensory inputs on TEPs. Results:The responses to the scalp and shoulder stimulations were correlated in both temporal and spatial domains, especially after ~60 ms, regardless of the intensity and stimuli waveform.Among the three filters, SSP-SIR showed the best trade-off between removing sensory-related signals while preserving data not related to the control condition. Conclusions:The findings demonstrate that TEPs elicited by motor cortex TMS reflect a combination of transcranially and peripherally evoked brain responses despite adopting sensory attenuation methods during experiments, thereby highlighting the importance of adopting sensory control conditions in TMS-EEG studies. Offline filters may help to isolate the transcranial component of the TEP from its peripheral component, but only if these components express different spatiotemporal patterns. More realistic control conditions may help to improve the characterization and attenuation of sensory inputs to TEPs, especially in early responses.

Probing regional cortical excitability via input–output properties using transcranial magnetic stimulation and electroencephalography coupling

“…The quality of regression of the realistic sham conditions on early components never significantly differed from noise, as quantified with the classical sham procedure used in the first experiment. This is in line with recent findings suggesting that only late components appear to contain significant PEPs (Biabani et al, ; Freedberg et al, ). In contrast, recruitment curves drawn from active stimulation differed from noise and showed different patterns across sites, possibly revealing different input–output properties of the cortical tissue.…”

A fast and general method to empirically estimate the complexity of brain responses to transcranial and intracranial stimulations

“…A key advantage of SPES over TMS is that the former is not associated with concurrent auditory and/or somatosensory stimulation [45]. The extent of the actual contribution of sensory co-stimulation to TEPs depends on many factors, such as coil type and effectiveness of noise masking, and is currently a matter of debate [46][47][48][49]. In this respect, the present intracranial Finally, the mesoscale assessment of perturbational complexity is in an ideal position to link microscale explorations at the bench to the macroscale measurements performed at the bedside of brain-injured patients.…”

Assessing cerebellar-cortical connectivity using concurrent TMS-EEG: A Feasibility Study

“…Spearman's rank correlation analyses were used to explore statistical relationships between the cleaned signals from different conditions in both spatial (i.e., across electrodes for each time point) and temporal domains (i.e., across time points for each electrode). For the temporal analyses, latencies were grouped into early (20-60 ms), mid (60-180 ms) and late (180-280 ms) time ranges [14]. To analyse these correlation values at the group level, a procedure based on Fisher's transform for dependent samples was used to convert the correlation statistics to z-values [14,36,37].…”

“…However, the temporal features of TMS-induced network-based transfer from locally activated Purkinje cells in the cerebellar cortex to cerebral regions via the cerebellarthalamocortical tract are not well characterised. Electroencephalography (EEG) has increasingly been used to quantify the direct cortical response to focal TMS, particularly in regions not amenable to peripheral motor assessment [10][11][12][13][14][15][16]. While a small number of studies have reported changes in cortical oscillations and TMS-evoked potentials (TEPs) following single-pulse cerebellar TMS, they did not always include appropriate controls to account for the large muscle and sensory artefacts that accompany the TMS pulse (see [17] for review).…”

“…While a small number of studies have reported changes in cortical oscillations and TMS-evoked potentials (TEPs) following single-pulse cerebellar TMS, they did not always include appropriate controls to account for the large muscle and sensory artefacts that accompany the TMS pulse (see [17] for review). Indeed, muscle and sensory artefacts are well documented in the TMS-EEG literature, and deemed notoriously difficult to separate from TEPs due to substantial spatial and temporal overlaps [12][13][14][18][19][20][21]. The non-linearity of these relationships necessitates the use of sophisticated methods to extract the salient features of the TMS-evoked signal.…”