Local brain-state dependency of effective connectivity: a pilot TMS-EEG study [version 2; peer review: 2 approved].

Transcranial magnetic stimulation brain state effective connectivity electroencephalography

Journal

Open research Europe
ISSN: 2732-5121
Titre abrégé: Open Res Eur
Pays: Belgium
ID NLM: 9918230081006676

Informations de publication

Date de publication:
11 Jul 2022
Historique:
entrez: 29 8 2022
pubmed: 30 8 2022
medline: 30 8 2022
Statut: ppublish

Résumé

Spontaneous cortical oscillations have been shown to modulate cortical responses to transcranial magnetic stimulation (TMS). However, whether these oscillations influence cortical effective connectivity is largely unknown. We conducted a pilot study to set the basis for addressing how spontaneous oscillations affect cortical effective connectivity measured through TMS-evoked potentials (TEPs). We applied TMS to the left primary motor cortex and right pre-supplementary motor area of three subjects while recording EEG. We classified trials off-line into positive- and negative-phase classes according to the mu and beta rhythms. We calculated differences in the global mean-field amplitude (GMFA) and compared the cortical spreading of the TMS-evoked activity between the two classes. Phase affected the GMFA in four out of 12 datasets (3 subjects × 2 stimulation sites × 2 frequency bands). Two of the observed significant intervals were before 50 ms, two between 50 and 100 ms, and one after 100 ms post-stimulus. Source estimates showed complex spatial differences between the classes in the cortical spreading of the TMS-evoked activity. TMS-evoked effective connectivity seems to depend on the phase of local cortical oscillations at the stimulated site. This work paves the way to design future closed-loop stimulation paradigms.

Sections du résumé

Background UNASSIGNED
Spontaneous cortical oscillations have been shown to modulate cortical responses to transcranial magnetic stimulation (TMS). However, whether these oscillations influence cortical effective connectivity is largely unknown. We conducted a pilot study to set the basis for addressing how spontaneous oscillations affect cortical effective connectivity measured through TMS-evoked potentials (TEPs).
Methods UNASSIGNED
We applied TMS to the left primary motor cortex and right pre-supplementary motor area of three subjects while recording EEG. We classified trials off-line into positive- and negative-phase classes according to the mu and beta rhythms. We calculated differences in the global mean-field amplitude (GMFA) and compared the cortical spreading of the TMS-evoked activity between the two classes.
Results UNASSIGNED
Phase affected the GMFA in four out of 12 datasets (3 subjects × 2 stimulation sites × 2 frequency bands). Two of the observed significant intervals were before 50 ms, two between 50 and 100 ms, and one after 100 ms post-stimulus. Source estimates showed complex spatial differences between the classes in the cortical spreading of the TMS-evoked activity.
Conclusions UNASSIGNED
TMS-evoked effective connectivity seems to depend on the phase of local cortical oscillations at the stimulated site. This work paves the way to design future closed-loop stimulation paradigms.

Identifiants

pubmed: 36035767
doi: 10.12688/openreseurope.14634.2
pmc: PMC7613446
mid: EMS152876
doi:

Types de publication

Journal Article

Langues

eng

Pagination

45

Subventions

Organisme : European Research Council
ID : 810377
Pays : International

Déclaration de conflit d'intérêts

Competing interests: P.L. has received consulting fees (unrelated to this work) from Nexstim Plc. R.J.I. is a minority shareholder of Nexstim Plc. The other authors declare no competing interests. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Auteurs

Ida Granö (I)

Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.
BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.

Tuomas P Mutanen (TP)

Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.
BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.

Aino Tervo (A)

Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.
BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.

Jaakko O Nieminen (JO)

Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.
BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.

Victor H Souza (VH)

Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.
BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
School of Physiotherapy, Federal University of Juiz de Fora, Juiz de Fora, MG, Brazil.

Matteo Fecchio (M)

Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy.
Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.

Mario Rosanova (M)

Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy.

Pantelis Lioumis (P)

Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.
BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.

Risto J Ilmoniemi (RJ)

Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.
BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.

Classifications MeSH