TMS-EEG Methodological advancement

Advancing TMS-EEG Methodologies for Probing Prefrontal Cortical Excitability and Inhibition

In my research on neuromodulation for psychiatric disorders, particularly treatment-resistant depression (TRD), I have contributed to several studies using transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) to characterize cortical responses in the dorsolateral prefrontal cortex (DLPFC). This work focuses on disentangling true cortical reactivity from sensory artifacts, optimizing stimulation parameters, and exploring how ongoing brain oscillations influence TMS outcomes. These efforts align with my goal of developing reliable biomarkers and personalized interventions to enhance therapeutic efficacy.

A foundational contribution was developing and validating a standardized protocol for neuronavigated TMS-EEG (nTMS-EEG) targeting the DLPFC (Lioumis et al., 2018). This method enables precise, reproducible assessment of cortical excitability and cortico-cortical connectivity, incorporating single-pulse TMS for excitability and paired-pulse paradigms for short intracortical inhibition (SICI), long intracortical inhibition (LICI), and intracortical facilitation (ICF). Applied in healthy volunteers and patients with depression, it facilitates test-retest paradigms to monitor changes from treatments like repetitive TMS (rTMS), magnetic seizure therapy (MST), and electroconvulsive therapy (ECT), while addressing artifacts through masking and careful coil positioning.

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TMS-EEG measures of cortical excitability. (A) Grand average of TMS-evoked EEG responses from DLPFC ROI electrodes after DLPFC stimulation. (B) N100 values plotted topographically across all electrodes for each session.

Building on this methodology, one key study examined the dose-response effects of intermittent theta-burst stimulation (iTBS) on DLPFC activity (Desforges et al., 2022). We compared 600, 1200, and 1800 pulses using TMS-EEG in healthy participants, finding no significant differences in modulation of TMS-evoked potentials (TEPs) or oscillatory activity across doses. However, all iTBS conditions altered TEP components (e.g., P30, N45, P60, P200) and reduced theta-band power, suggesting a common mechanism involving excitation-inhibition balance. This implies that higher doses may not necessarily amplify prefrontal potentiation, informing clinical protocols for TRD. To promote reproducibility, I have made the Matlab code for preprocessing and analyzing significant current density (SCD) and current scattering (SCS) metrics from this study openly available in my GitHub repository: github.com/ItayHadas/DLPFC_SCD_SCS_analysis. The repository includes automated TMS-EEG pipelines (based on the AARATEP Pipeline Cline et al., 2021), Brainstorm-based source localization and time-series analysis scripts, and statistical analysis tools like repeated-measures ANOVA for evaluating iTBS effects between diffrent doses and study phases.

Building on this methodology, one key study examined the dose-response effects of intermittent theta-burst stimulation (iTBS) on DLPFC activity (Desforges et al., 2022). We compared 600, 1200, and 1800 pulses using TMS-EEG in healthy participants, finding no significant differences in modulation of TMS-evoked potentials (TEPs) or oscillatory activity across doses. However, all iTBS conditions altered TEP components (e.g., P30, N45, P60, P200) and reduced theta-band power, suggesting a common mechanism involving excitation-inhibition balance. This implies that higher doses may not necessarily amplify prefrontal potentiation, informing clinical protocols for TRD.

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Averaged single-pulse TEP waveform from the three iTBS doses and obtained from the ROI (AF3, F1, F3, FC1, F3). (A) The top panel displays the grand average waveform pre iTBS (continuous line) and post iTBS (dotted line). Shaded area around the curves correspond to the standard error of the mean. Grey shaded areas correspond to the time window of each peak of interest. The missing waveform from 0 to 15 ms corresponds to the data that was removed and interpolated after the TMS pulse. Significant modulations of TEP amplitude post iTBS are obtained for components P30, N45, P60 and P200. (B) The bottom panel shows topographical plots of voltage scalp distribution, for each time window of interest. P30-N45-P60 components activity is localised in the left prefrontal region, while N100 and P200 shows fronto-central activity. ** p < 0.001
Centered image
The figure illustrates a general reduction in SCD post-iTBS across doses, with stronger plasticity effect at the highest pulse number (1800 pulses). SCD time courses pre-iTBS (blue) and post-iTBS (red) for 600, 1200, and 1800 pulses over 0-400 ms (top row), with shaded error bands, grey shade segment indicates the early TMS activation timing taken for the statistics. Bottom row includes estimated marginal means of DLPFC SCD (TMS position) pre-iTBS and post-iTBS by dose (left), SCD vs. dose estimated marginal means scatter (middle), and bar plots of pre-iTBS vs. post-iTBS for each dose with error bars and repeated ANOVA (+) significance markers (right).
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Averaged paired-corrected ERSP from the three iTBS conditions. (A) The top panel shows SICI and the black lines set at time −2 and 0 represents the TMS pulses. (B) The bottom panel represents LICI and the black vertical lines set at time −100 and 0 represents the TMS pulses. For both panels, pre and post iTBS are presented. Each dotted block corresponds to the frequency bands of interest and their specific time window, either theta, alpha, beta and gamma. At baseline, t-tests revealed significant differences between single-pulse and paired-pulse data for all frequency bands. Linear mixed model showed a main effect of time-point for LICI of theta. ****p < 0.0001, #p < 0.05; ERSP = event-related spectral perturbation

We also addressed sensory confounds in TMS-EEG signals. In Poorganji et al. (Poorganji et al., 2021), we differentiated cortical from auditory responses using single-pulse (SP) and paired-pulse (PP; LICI) protocols over DLPFC. Active TMS elicited larger TEPs (e.g., N100-P200 amplitude) and broader oscillatory inhibition compared to sham, confirming that true cortical effects dominate when artifacts are controlled. Similarly, in Poorganji et al. (Poorganji et al., 2023), we isolated somatosensory and auditory artifacts during suprathreshold stimulation, showing that masking (e.g., foam spacers and noise) attenuates non-cortical contributions while preserving significant cortical excitability and inhibition metrics like cortical evoked activity (CEA) and global mean field amplitude (GMFA).

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Topoplot of (a) area under the curve for single pulse (SP) active stimulation, (b) area under the curve for SP sham stimulation, (c) inhibition caused by active long interval intracortical inhibition (LICI), (d) inhibition caused by sham LICI. The same pattern of spatial activity in both active and sham SP (a and b) with approximately 3 folds of the level of activation in active stimulation compared to sham stimulation. The graphs are the average response for all 19 participants.
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(a) TMS-evoked Potentials (TEPs) from active and sham single pulse (SP) stimulation. (b) TEPs from active SP and corrected Paired Pulse (PP) stimulation. (c) TEPs from sham SP and corrected PP stimulation. The TEPs are the average response for all 19 participants.

Further, we investigated how pre-stimulus brain states affect TMS responses. In Poorganji et al. (Poorganji et al., 2023), analyzing 64 datasets from healthy participants, we found that pre-TMS oscillatory power (but not phase) in theta and beta bands predicted DLPFC excitability. High-power states amplified post-TMS activity, and we introduced a “corrected_effect” metric to isolate TMS-specific contributions from spontaneous oscillations. This highlights the potential for power-thresholded TMS to reduce response variability.

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(a) A sample power spectrum for the frequency bands of theta (4–7 Hz), alpha (8–13 Hz), and beta (14–30 Hz); (b) error of phase estimation, using PHASTIMATE toolbox (second column) compared with Hilbert transform (first column) in all the trials, in each frequency band, and without applying the power threshold; (c) error of phase estimation in all the trials which passed the power threshold (mean of power in each frequency band and for every subject). Error of estimation was measured using circular standard deviation [31]. The phase in all the plots is estimated for 1000 ms before TMS pulse.

These studies, where I contributed to experimental design, data analysis (e.g., EEG source localization, connectivity), protocol development, and biomarker validation, underscore TMS-EEG’s value for mechanistic insights into prefrontal circuits associated with mental conditions. Looking ahead, I aim to extend this to clinical populations, integrating multimodal data (e.g., fMRI, EEG) for predictive biomarkers in TRD trials, ultimately enabling state-informed, personalized neuromodulation.

References

2023

  1. Isolating sensory artifacts in the suprathreshold TMS-EEG signal over DLPFC
    Mohsen Poorganji, Reza Zomorrodi, Colin Hawco, and 8 more authors
    Scientific Reports, Apr 2023
  2. Pre-Stimulus Power but Not Phase Predicts Prefrontal Cortical Excitability in TMS-EEG
    Mohsen Poorganji, Reza Zomorrodi, Christoph Zrenner, and 10 more authors
    Biosensors, Feb 2023
    Number: 2 Publisher: Multidisciplinary Digital Publishing Institute

2022

  1. Dose-response of intermittent theta burst stimulation of the prefrontal cortex: A TMS-EEG study
    Manon Desforges, Itay Hadas, Brian Mihov, and 8 more authors
    Clinical Neurophysiology, Jan 2022

2021

  1. Differentiating transcranial magnetic stimulation cortical and auditory responses via single pulse and paired pulse protocols: A TMS-EEG study
    Mohsen Poorganji, Reza Zomorrodi, Colin Hawco, and 8 more authors
    Clinical Neurophysiology, Aug 2021
    Number: 8 0 citations (Crossref) [2021-06-20]

2018

  1. Combined Transcranial Magnetic Stimulation and Electroencephalography of the Dorsolateral Prefrontal Cortex
    Pantelis Lioumis, Reza Zomorrodi, Itay Hadas, and 2 more authors
    JoVE (Journal of Visualized Experiments), Aug 2018
    Number: 138