Identifying site- and stimulation-specific TMS-evoked EEG potentials using a quantitative cosine similarity metric

Autoři: Michael Freedberg aff001;  Jack A. Reeves aff001;  Sara J. Hussain aff003;  Kareem A. Zaghloul aff004;  Eric M. Wassermann aff001
Působiště autorů: Behavioral Neurology Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States of America aff001;  Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States of America aff002;  Human Cortical Physiology and Neurorehabilitation Section, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States of America aff003;  Functional and Restorative Neurosurgery Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States of America aff004
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0216185


The ability to interpret transcranial magnetic stimulation (TMS)-evoked electroencephalography (EEG) potentials (TEPs) is limited by artifacts, such as auditory evoked responses produced by discharge of the TMS coil. TEPs generated from direct cortical stimulation should vary in their topographical activity pattern according to stimulation site and differ from responses to sham stimulation. Responses that do not show these effects are likely to be artifactual. In 20 healthy volunteers, we delivered active and sham TMS to the right prefrontal, left primary motor, and left posterior parietal cortex and compared the waveform similarity of TEPs between stimulation sites and active and sham TMS using a cosine similarity-based analysis method. We identified epochs after the stimulus when the spatial pattern of TMS-evoked activation showed greater than random similarity between stimulation sites and sham vs. active TMS, indicating the presence of a dominant artifact. To do this, we binarized the derivatives of the TEPs recorded from 30 EEG channels and calculated cosine similarity between conditions at each time point with millisecond resolution. Only TEP components occurring before approximately 80 ms differed across stimulation sites and between active and sham, indicating site and condition-specific responses. We therefore conclude that, in the absence of noise masking or other measures to decrease neural artifact, TEP components before about 80 ms can be safely interpreted as stimulation location-specific responses to TMS, but components beyond this latency should be interpreted with caution due to high similarity in their topographical activity pattern.

Klíčová slova:

Cosine similarity – Electroencephalography – Evoked potentials – Functional electrical stimulation – Interpolation – Prefrontal cortex – Scalp – Transcranial magnetic stimulation


1. Massimini M, Ferrarelli F, Huber R, Esser SK, Singh H, Tononi G. Breakdown of cortical effective connectivity during sleep. Science. 2005;309: 2228–2232. doi: 10.1126/science.1117256 16195466

2. Ferrarelli F, Massimini M, Sarasso S, Casali A, Riedner BA, Angelini G, et al. Breakdown in cortical effective connectivity during midazolam-induced loss of consciousness. Proc Natl Acad Sci. 2010;107: 2681–2686. doi: 10.1073/pnas.0913008107 20133802

3. Rogasch NC, Daskalakis ZJ, Fitzgerald PB. Cortical inhibition of distinct mechanisms in the dorsolateral prefrontal cortex is related to working memory performance: A TMS-EEG study. Cortex. 2015;64: 68–77. doi: 10.1016/j.cortex.2014.10.003 25461708

4. Bruckmann S, Hauk D, Roessner V, Resch F, Freitag CM, Kammer T, et al. Cortical inhibition in attention deficit hyperactivity disorder: New insights from the electroencephalographic response to transcranial magnetic stimulation. Brain. 2012;135: 2215–2230. doi: 10.1093/brain/aws071 22492560

5. Ferreri F, Vecchio F, Vollero L, Guerra A, Petrichella S, Ponzo D, et al. Sensorimotor cortex excitability and connectivity in Alzheimer’s disease: A TMS-EEG co-registration study. Hum Brain Mapp. 2016;37: 2083–2096. doi: 10.1002/hbm.23158 26945686

6. Frantseva M, Cui J, Farzan F, Chinta L V., Perez Velazquez JL, Daskalakis ZJ. Disrupted cortical conductivity in schizophrenia: TMS-EEG study. Cereb Cortex. 2014;24: 211–221. doi: 10.1093/cercor/bhs304 23042743

7. Rosanova M, Casali A, Bellina V, Resta F, Mariotti M, Massimini M. Natural frequencies of human corticothalamic circuits. J Neurosci. 2009;29: 7679–7685. doi: 10.1523/JNEUROSCI.0445-09.2009 19535579

8. Darmani G, Zipser CM, Böhmer GM, Deschet K, Müller-Dahlhaus F, Belardinelli P, et al. Effects of the selective α5-GABAAR antagonist S44819 on excitability in the human brain: A TMS–EMG and TMS–EEG phase I study. J Neurosci. 2016;36: 12312–12320. doi: 10.1523/JNEUROSCI.1689-16.2016 27927951

9. Bortoletto M, Veniero D, Thut G, Miniussi C. The contribution of TMS-EEG coregistration in the exploration of the human cortical connectome. Neurosci Biobehav Rev. 2015;49: 114–124. doi: 10.1016/j.neubiorev.2014.12.014 25541459

10. Siebner HR, Conde V, Tomasevic L, Thielscher A, Bergmann TO. Distilling the essence of TMS-evoked EEG potentials (TEPs): A call for securing mechanistic specificity and experimental rigor. Brain Stimul. 2019;12: 1051–1054. doi: 10.1016/j.brs.2019.03.076 30962028

11. Belardinelli P, Biabani M, Blumberger DM, Bortoletto M, Casarotto S, David O, et al. Reproducibility in TMS–EEG studies: A call for data sharing, standard procedures and effective experimental control. Brain Stimul. 2019;12: 787–790. doi: 10.1016/j.brs.2019.01.010 30738777

12. Rogasch NC, Sullivan C, Thomson RH, Rose NS, Bailey NW, Fitzgerald PB, et al. NeuroImage Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open-source TESA software. Neuroimage. 2017;147: 934–951. doi: 10.1016/j.neuroimage.2016.10.031 27771347

13. Rogasch NC, Thomson RH, Farzan F, Fitzgibbon BM, Bailey NW, Hernandez-pavon JC, et al. Removing artefacts from TMS-EEG recordings using independent component analysis: Importance for assessing prefrontal and motor cortex network properties. Neuroimage. 2014;101: 425–439. doi: 10.1016/j.neuroimage.2014.07.037 25067813

14. Foxe JJ, Wylie GR, Martinez A, Schroeder CE, Javitt DC, Guilfoyle D, et al. Auditory-somatosensory multisensory processing in auditory association cortex: An fMRI study. J Neurophysiol. 2002;88: 540–543. doi: 10.1152/jn.2002.88.1.540 12091578

15. Molholm S, Ritter W, Murray MM, Javitt DC, Schroeder CE, Foxe JJ. Multisensory auditory-visual interactions during early sensory processing in humans: A high-density electrical mapping study. Cogn Brain Res. 2002;14: 115–128. doi: 10.1016/S0926-6410(02)00066-6

16. Stam CJ. Nonlinear brain dynamics. Nova Biomedical. 2006.

17. ter Braack EM, de Vos CC, van Putten MJ. Masking the auditory evoked potential in TMS-EEG: A comparison of various methods. Brain Topogr. 2015;28: 520–528. doi: 10.1007/s10548-013-0312-z 23996091

18. Conde V, Tomasevic L, Akopian I, Stanek K, Saturnino GB, Thielscher A, et al. The non-transcranial TMS-evoked potential is an inherent source of ambiguity in TMS-EEG studies. Neuroimage. 2019;185: 300–312. doi: 10.1016/j.neuroimage.2018.10.052 30347282

19. Du X, Choa F Sen, Summerfelt A, Rowland LM, Chiappelli J, Kochunov P, et al. N100 as a generic cortical electrophysiological marker based on decomposition of TMS-evoked potentials across five anatomic locations. Exp Brain Res. 2017;235: 69–81. doi: 10.1007/s00221-016-4773-7 27628235

20. Harquel S, Bacle T, Beynel L, Marendaz C, Chauvin A, David O. Mapping dynamical properties of cortical microcircuits using robotized TMS and EEG: Towards functional cytoarchitectonics. Neuroimage. 2016;135: 115–124. doi: 10.1016/j.neuroimage.2016.05.009 27153976

21. Nikouline V, Ruohonen J, Ilmoniemi RJ. The role of the coil click in TMS assessed with simultaneous EEG. Clin Neurophysiol. 1999;110: 1325–1328. doi: 10.1016/s1388-2457(99)00070-x 10454266

22. Herring JD, Thut G, Jensen O, Bergmann TO. Attention modulates TMS-locked alpha oscillations in the visual cortex. J Neurosci. 2015;35: 14435–14447. doi: 10.1523/JNEUROSCI.1833-15.2015 26511236

23. Yaffe RB, Kerr MSD, Damera S, Sarma S V., Inati SK, Zaghloul KA. Reinstatement of distributed cortical oscillations occurs with precise spatiotemporal dynamics during successful memory retrieval. Proc Natl Acad Sci. 2014;111: 18727–18732. doi: 10.1073/pnas.1417017112 25512550

24. Salton G, Wong A, Yang CS. A vector space model for information retrieval. Commun ACM. 1975;18: 613–630.

25. Poldrack RA, Clark J, Pare-Blagoev EJ, Shohamy D, Moyano JC, Myers C, et al. Interactive memory systems in the human brain. Nature. 2001;414: 546–550. doi: 10.1038/35107080 11734855

26. Koch G, Ponzo V, Di Lorenzo F, Caltagirone C, Veniero D. Hebbian and anti-hebbian spike-timing-dependent plasticity of human cortico-cortical connections. J Neurosci. 2013;33: 9725–9733. doi: 10.1523/JNEUROSCI.4988-12.2013 23739969

27. Cox RW. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res. 1996;29: 162–173. doi: 10.1006/cbmr.1996.0014 8812068

28. Mills KR, Boniface SJ, Schubert M. Magnetic brain stimulation with a double coil: The importance of coil orientation. Electroencephalogr Clin Neurophysiol. 1992;85: 17–21. doi: 10.1016/0168-5597(92)90096-t 1371739

29. Awiszus F, Borckardt J. TMS Motor Threshold Assessment Tool (MTAT 2.0). 2011.

30. Hyvärinen A, Oja E. Independent component analysis: Algorithms and applications. Neural Networks. 2000;13: 411–430. doi: 10.1016/s0893-6080(00)00026-5 10946390

31. Hallgren KA. Computing inter-rater reliability for observational data: An overview and tutorial. Tutor Quant Methods Psychol. 2012;8: 23–34. doi: 10.20982/tqmp.08.1.p023 22833776

32. McHugh ML. Interrater reliability: The kappa statistic. Biochem Medica. 2012; 276–282. doi: 10.11613/BM.2012.031

33. Hjorth B. An on-line transformation of EEG scalp potentials into orthogonal source derivations. Electroencephalogr Clin Neurophysiol. 1975;39: 526–530. doi: 10.1016/0013-4694(75)90056-5 52448

34. Maris E, Oostenveld R. Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods. 2007;164: 177–190. doi: 10.1016/j.jneumeth.2007.03.024 17517438

35. Näätänen R, Picton T. The N1 wave of the human electric and magnetic response to sound: A review and an analysis of the component structure. Psychophysiology. 1987;24: 375–425. doi: 10.1111/j.1469-8986.1987.tb00311.x 3615753

36. Spiegler A, Hansen ECA, Bernard C, McIntosh AR, Jirsa VK. Selective activation of resting-state networks following focal stimulation in a connectome-based network model of the human brain. eNeuro. 2016;3. doi: 10.1523/ENEURO.0068-16.2016 27752540

37. Lioumis P, Kičić D, Savolainen P, Mäkelä JP, Kähkönen S. Reproducibility of TMS-evoked EEG responses. Hum Brain Mapp. 2009;30: 1387–1396. doi: 10.1002/hbm.20608 18537115

38. Kerwin LJ, Keller CJ, Wu W, Narayan M, Etkin A. Test-retest reliability of transcranial magnetic stimulation EEG evoked potentials. Brain Stimul. 2017; doi: 10.1016/j.brs.2017.12.010 29342443

39. Soderlund GBW, Björk C, Gustafsson P. Comparing auditory noise treatment with stimulant medication on cognitive task performance in children with attention deficit hyperactivity disorder: Results from a pilot study. Front Psychol. 2016;7. doi: 10.3389/fpsyg.2016.00007

40. Furnham A, Strbac L. Music is as distracting as noise: The differential distraction of background music and noise on the cognitive test performance of introverts and extraverts. Ergonomics. 2002;45: 203–217. doi: 10.1080/00140130210121932 11964204

41. Herweg NA, Bunzeck N. Differential effects of white noise in cognitive and perceptual tasks. Front Psychol. 2015;6. doi: 10.3389/fpsyg.2015.01639 26579024

42. Michel CM, Murray MM, Lantz G, Gonzalez S, Spinelli L, Grave De Peralta R. EEG source imaging. Clin Neurophysiol. 2004;115: 2195–2222. doi: 10.1016/j.clinph.2004.06.001 15351361

43. Garcia JO, Grossman ED, Srinivasan R. Evoked potentials in large-scale cortical networks elicited by TMS of the visual cortex. J Neurophysiol. 2011;106: 1734–1746. doi: 10.1152/jn.00739.2010 21715670

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