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Locating activation sites of TMS with opposite current directions using probabilistic modelling and biophysical axon models

 
cris.virtual.department#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.department5e44ddde-1363-426a-bd9f-b67fc5a7d75c
cris.virtualsource.orcid5e44ddde-1363-426a-bd9f-b67fc5a7d75c
dc.contributor.authorLaakso, Ilkka
dc.contributor.authorKataja, Juhani
dc.contributor.authorMatilainen, Noora
dc.contributor.authorRoine, Timo
dc.contributor.authorTarnaud, Thomas
dc.contributor.authorUgawa, Yoshikazu
dc.date.accessioned2025-03-22T07:20:25Z
dc.date.available2025-03-22T07:20:25Z
dc.date.issued2025
dc.description.abstractBackground: Motor responses evoked by transcranial magnetic stimulation (TMS) using posterior–anterior (PA) and anterior–posterior (AP) current directions have distinct latencies and thresholds. However, the underlying reasons for these differences remain unclear. Objective: To quantify the differences in activation sites between PA- and AP-TMS. Methods: Motor evoked potentials (MEPs) were recorded from five hand and arm muscles in nine healthy participants using both PA- and AP-TMS. Active motor thresholds were determined at 11 magnetic coil positions on the scalp. Probabilistic modelling was used to combine the measured threshold data with calculated electric field data from individual MRI-based models. This approach constructed 70 probability distributions of the activation site, dependent on the muscle and TMS direction. Results: Modelling indicated that both PA- and AP-TMS more likely activated structures in white matter than in grey matter. PA-TMS activation sites were primarily in the white or grey matter in the precentral gyrus, while the AP-TMS activations were deeper and more posterior and lateral, likely within white matter under the postcentral and/or precentral gyri. Tractography and biophysical axon models provided a potential explanation on the location of activation sites: AP-TMS may activate the bends of white matter axons farther from M1 than PA-TMS, such that the conduction velocity along the neural tract could potentially explain the longer MEP latency of AP-TMS. The differences in activation sites among the five hand and arm muscles were small. Conclusion: While a direct experimental confirmation of the activation sites is still needed, the results suggest that electric field analysis combined with tractography and biophysical axon modelling could be a useful computational tool for analysing and optimizing TMS.
dc.description.wosFundingTextThis work was supported by Research Council of Finland [grant nos. 325326 and 362610] .
dc.identifier.doi10.1016/j.brs.2025.02.003
dc.identifier.issn1935-861X
dc.identifier.pmidMEDLINE:39955027
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/45434
dc.publisherELSEVIER SCIENCE INC
dc.source.beginpage215
dc.source.endpage224
dc.source.issue2
dc.source.journalBRAIN STIMULATION
dc.source.numberofpages10
dc.source.volume18
dc.subject.keywordsTRANSCRANIAL MAGNETIC STIMULATION
dc.subject.keywordsSURFACE-BASED ANALYSIS
dc.subject.keywordsSTIMULUS WAVE-FORMS
dc.subject.keywordsHUMAN MOTOR CORTEX
dc.subject.keywordsELECTRICAL-STIMULATION
dc.subject.keywordsDIFFUSION TENSOR
dc.subject.keywordsCELL-BODIES
dc.subject.keywordsMECHANISMS
dc.subject.keywordsDEPENDENCE
dc.subject.keywordsSYSTEM
dc.title

Locating activation sites of TMS with opposite current directions using probabilistic modelling and biophysical axon models

dc.typeJournal article
dspace.entity.typePublication
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