The DOI matches its Crossref record; 57 of the 59 references it cites were found in a registry or an index.
Characterizing and minimizing the contribution of sensory inputs to TMS-evoked potentials
Open accessPublished version of a preprint
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 sensoryrelated 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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“While TEPs (particularly later TEPs) have been demonstrated to be influenced by somatosensory and auditory processing of the TMS pulse (Conde et al., 2019;Nikouline et al. 1999;Biabani et al. 2019;Biabani et al. 2021)”
From Investigation of neurobiological responses to theta burst stimulation during recovery from mild traumatic brain injury (mTBI) (2023) · cites this paper as Biabani et al. 2019
The cited source says what the claim attributes to it.
In the citing paper
“However, given the high likelihood of sensory contributions to the late components (Biabani et al. 2019, Conde et al. 2019, Biabani et al. 2021, Gordon et al. 2021, Rocchi et al. 2021, Biabani et al. 2024), TEP quantification focussed exclusively on the early peaks.”
From Mild traumatic brain injury alters function in the dorsolateral prefrontal cortex: a TMS-EEG study (2025) · cites this paper as Biabani et al. 2019
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/156869 · Cited by 10,323
Textbook of Medical Physiology
Guyton, A. C., & Hall, J. E. (1966) · Annals of Internal Medicine · doi:10.7326/0003-4819-64-6-1363_2 · Cited by 9,760
Independent component analysis: algorithms and applications
Hyvärinen, A., & Erkki Oja, A. (2000) · Neural Networks · doi:10.1016/s0893-6080(00)00026-5 · Cited by 8,926
Rossi, S., Hallett, M., Rossini, P. M., & Pascual-Leone, A. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2009.08.016 · Cited by 5,367
Brainstorm: A User-Friendly Application for MEG/EEG Analysis
Tadel, F., Baillet, S., Mosher, J., Pantazis, D., & Leahy, R. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/879716 · Cited by 3,798
Rossini, P., Burke, D. F., Chen, R., Cohen, L., Daskalakis, Z., Iorio, R. D., di Lazzaro, V., Ferreri, F., Fitzgerald, P., George, M., et al. (2015) · Clinical Neurophysiology · doi:10.1016/j.clinph.2015.02.001 · Cited by 2,729
Breakdown of cortical effective connectivity during sleep
Massimini, M., Ferrarelli, F., Huber, R., Esser, S. K., Singh, H., & Tononi, G. (2005) · Science · doi:10.1126/science.1117256 · Cited by 1,534
OpenMEEG: opensource software for quasistatic bioelectromagnetics
Gramfort, A., Papadopoulo, T., Olivi, E., & Clerc, M. (2010) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-9-45 · Cited by 1,162
Mangun, G., & Hillyard, S. (1991) · Journal of Experimental Psychology: Human Perception and Performance · doi:10.1037//0096-1523.17.4.1057 · Cited by 968
Research works printed but not linked: 2
- Conde, 2018, The non-transcranial TMS-evoked potential is an inherent source of ambiguity in TMS-EEG studies, Neuroimage
- Rothwell, 1999, Magnetic stimulation: motor evoked potentials, Electroencephalogr Clin Neurophysiol Suppl, 52, 97
Cited by
Sorted by citations
TMS combined with EEG: Recommendations and open issues for data collection and analysis
Hernandez-Pavon, J. C., Veniero, D., Bergmann, T. O., Belardinelli, P., Bortoletto, M., Casarotto, S., Casula, E. P., Farzan, F., Fecchio, M., Julkunen, P., et al. (2023) · Brain Stimulation · doi:10.1016/j.brs.2023.02.009 · Cited by 238
Self-citationDisentangling EEG responses to TMS due to cortical and peripheral activations
Rocchi, L., Santo, A. D., Brown, K. E., Ibáñez, J., Casula, E. P., Rawji, V., Lazzaro, V. D., Koch, G., & Rothwell, J. C. (2020) · Brain stimulation · doi:10.1016/j.brs.2020.10.011 · Cited by 202
Casarotto, S., Fecchio, M., Rosanova, M., Varone, G., D’Ambrosio, S., Sarasso, S., Pigorini, A., Russo, S., Comanducci, A., Ilmoniemi, R. J., et al. (2022) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2022.109486 · Cited by 134
The impact of artifact removal approaches on TMS–EEG signal
Bertazzoli, G., Esposito, R., Mutanen, T. P., Ferrari, C., Ilmoniemi, R. J., Miniussi, C., & Bortoletto, M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118272 · Cited by 82
Self-citationAhn, S., & Frӧhlich, F. (2021) · Brain stimulation · doi:10.1016/j.brs.2021.01.018 · Cited by 82
Metadata
- Authors
- Biabani, M., Fornito, A., Mutanen, T. P., Morrow, J., & Rogasch, N. C.
- Affiliations
- Monash University (AU)
- University of Glasgow (GB)
- Corresponding author
- Biabani, Mana
- Venue
- Brain Stimulation
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 12
- Issue
- 6
- Pages
- 1537-1552
- ISSN
- 1876-4754 (ISSN-L)
- 1935-861X
- OpenAlex
- W2959546607
- Cited by
- 158
- References
- 59
- Citations by year
- 2026: 15 · 2025: 20 · 2024: 15 · 2023: 34 · 2022: 32 · 2021: 26 · 2020: 11 · 2019: 3
- FWCI
- 7.94
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Brain stimulation (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Australian Research Council
- National Health and Medical Research Council
- Topics
- Life Sciences › Neuroscience › Neurology › Transcranial Magnetic Stimulation Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- TMS-evoked potentials, sensory attenuation, TMS-EEG, motor cortex stimulation, independent component analysis, noise masking, spatio-temporal correlation, control conditions
- MeSH terms
- Adolescent, Adult, Electroencephalography, Female, Humans, Male, Motor Cortex, Evoked Potentials, Motor, Transcranial Magnetic Stimulation, Young Adult, Healthy Volunteers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
