TMS Methods

Non-invasive Brain Stimulation (TMS and TMS-EEG) Methods

Combining TMS with EEG can be very powerful in understanding normal brain network interaction, as well as abnormal brain network activity. Particularly promising are multisensory and sensorimotor frameworks for TMS-EEG. However, there are numerous methodological hurdles to using TMS-EEG, cleaning the data, and interpretation of the data. I develop methods for analysis of TMS-evoked and -induced network activity. My work examines sensory contributions to the TMS-EEG signal, called the Vertex Potential (VP, also called the auditory evoked potential, AEP). In one paper, (Ross et al., 2022, Scientific Reports), we developed and tested an Independent Components Analysis-based technique for isolating the VP and show that the TEP remaining after removal of VP is unique to stimulation site and to individual subjects and can provide insight into TMS-evoked potentials as well as other-modulated oscillatory dynamics. Due to multisensory or non-model contributions to the VP, an ICA-based removal technique may not always be appropriate. For these cases, we provide another method for dealing with the VP. We show that additional multisensory masking, as well as using a predictable TMS pulse timing, can reduce the VP and reports of perception significantly more than the most commonly used masking protocol (Ross et al., 2022, Human Brain Mapping). We call this combination the ATTENUATE protocol. It uses concepts from sensory neuroscience to minimize the sensory potential in the TMS-EEG. We show superiority with ATTENUATE to standard masking techniques.

For the development of causal TMS-EEG metrics, including for biomarkers of psychiatric disease, it is essential to maximize signal-to-noise by increasing the non-peripherally activated brain signals and minimize all other brain and non-brain signals unrelated to TMS causation. In a recent review paper, we explore the importance of reliability and validity metrics in the development of TMS-EEG biomarkers and provide specific recommendations (Parmigiani et al., In press). For stimulation of the prefrontal cortex, we demonstrate TMS-EEG optimization, showing that the TMS-EEG signal may have the best signal-to-noise for targets that are more posterior and medial than targets that are more anterior and lateral, and that a personalized approach to targeting can boost the the signal by >50% (Gogulski et al., Submitted).

Repetitive TMS (rTMS) is an effective treatment for major depressive disorder (MDD), obsessive-compulsive disorder, smoking cessation, and migraines. However, MDD remission rates remain suboptimal and advances in our understanding of the neural changes with different rTMS parameters are needed to provide foundational knowledge to guide the next generation of more effective and efficient treatments. We examine the efficacy of using single pulse TMS-EEG to probe network modulation after individual rTMS trains (Ross et al., In prep). In this work, using a randomized, controlled, crossover study, we show that TMS trains result in TMS-EEG effects non-local to the site of stimulation after 100 ms and local to the site of stimulation before 50 ms in oscillatory phase dynamics using Dynamical Systems Theory (Ross et al., In prep).

References:
[1] Ross, J.M., Sarkar, M., & Keller, C.J. (2022). Experimental suppression of transcranial magnetic stimulation-electroencephalography sensory potentials. Human Brain Mapping, 1-3. doi: 10.1002/hbm.25990
[2] Ross, J.M., Ozdemir, R.A., Lian, S.J., Fried, P.J., Schmitt, E.M., Inouye, S.K., Pascual-Leone, A., & Shafi, M.M. (2022). A structured ICA-based process for removing auditory-evoked potentials. Scientific Reports, 12, 1391.
[3] Ashburn, S.M., Abugaber, D., Antony, J.W., Bennion, K.A., Bridwell, D., Cardenas-Iniguez, C., Doss, M., Fernández, L., Huijsmans, I., Krisst, L., Lapate, R., Layher, E., Leong, J., Li, Y., Marquez, F., Munoz-Rubke, F., Musz, E., Patterson, T.K., Powers, J.P., Proklova, D., Rapuano, K.M., Robinson, C.S.H., Ross, J.M., Samaha, J., Sazma, M., Stewart, A.X., Stickel, A., Stolk, A., Vilgis, V., Zirnstein, M. (2020). Toward a socially responsible, transparent, and reproducible cognitive neuroscience. In M. Gazzaniga & R. Mangun (Eds.), The Cognitive Neurosciences VI. Cambridge, MA: MIT Press.
[4] Parmigiani, S., Ross, J.M., Cline, C., Minasi, C., Gogulski, J. Keller, C.J. (2022). Reliability and validity of TMS-EEG biomarkers. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging.
[5] Gogulski, J., Cline, C.C., Ross, J.M., Truong, J., Sarkar, M., Parmigiani, S., Keller, C.J. (Submitted). Mapping cortical excitability in the human dorsolateral prefrontal cortex.
[6] Ross, J.M., Cline, C.C., Sarkar, M., Truong, J., Keller, C.J. (In prep). Neural effects of TMS trains on the human prefrontal cortex.


Figure 1. TMS-evoked potentials (TEPs) before and after ICA-based removal of sensory vertex potential in TMS-EEG.

The VP, also called the auditory evoked potential (AEP), is evoked by the sound of single pulses of TMS. We developed and tested an Independent Components Analysis-based technique for isolating the VP. We show that the TMS-evoked potential (TEP) remaining after removal of VP using this technique is unique to stimulation site and to individual subjects and may contain other-modulated oscillatory dynamics in the alpha band. Figure adapted from Ross et al., 2022, Sci. Rep.

Figure 2. The ATTENUATE protocol uses concepts from sensory neuroscience to minimize the VP in the TEP during data collection.

Due to multisensory or non-model contributions to the VP, our ICA-based removal technique may not always be appropriate. For these cases, we show that additional multisensory masking, as well as using a predictable TMS pulse timing, can reduce the VP and reports of perception significantly more than the most commonly used masking protocol. Figure adapted from Ross et al., 2022, Hum. Brain Mapp.

Figure 3. We recommend scrutinized for both reliability and validity when developing TMS-EEG metrics.

Both reliability and validity should be assessed for all TMS-EEG metrics, including for biomarkers of psychiatric disease. This is because it is possible to have high reliability but low validity or high validity but low reliability. Figure from Parmigiani et al., In press, Biol. Psychiatry Cogn. Neurosci. Neuroimaging.