Neural mechanisms underlying improved new-word learning with high-density transcranial direct current stimulation.

Functional connectivity (FC) Left inferior frontal gyrus (IFG) Left temporo-parietal junction (TPJ) New-word learning Transcranial direct current stimulation (tDCS)

Journal

NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515

Informations de publication

Date de publication:
16 May 2024
Historique:
received: 25 01 2024
revised: 04 04 2024
accepted: 14 05 2024
medline: 18 5 2024
pubmed: 18 5 2024
entrez: 17 5 2024
Statut: aheadofprint

Résumé

Neurobehavioral studies have provided evidence for the effectiveness of anodal tDCS on language production, by stimulation of the left Inferior Frontal Gyrus (IFG) or of left Temporo-Parietal Junction (TPJ). However, tDCS is currently not used in clinical practice outside of trials, because behavioral effects have been inconsistent and underlying neural effects unclear. Here, we propose to elucidate the neural correlates of verb and noun learning and to determine if they can be modulated with anodal high-definition (HD) tDCS stimulation. Thirty-six neurotypical participants were randomly allocated to anodal HD-tDCS over either the left IFG, the left TPJ, or sham stimulation. On day one, participants performed a naming task (pre-test). On day two, participants underwent a new-word learning task with rare nouns and verbs concurrently to HD-tDCS for 20 min. The third day consisted of a post-test of naming performance. EEG was recorded at rest and during naming on each day. Verb learning was significantly facilitated by left IFG stimulation. HD-tDCS over the left IFG enhanced functional connectivity between the left IFG and TPJ and this correlated with improved learning. HD-tDCS over the left TPJ enabled stronger local activation of the stimulated area (as indexed by greater alpha and beta-band power decrease) during naming, but this did not translate into better learning. Thus, tDCS can induce local activation or modulation of network interactions. Only the enhancement of network interactions, but not the increase in local activation, leads to robust improvement of word learning. This emphasizes the need to develop new neuromodulation methods influencing network interactions. Our study suggests that this may be achieved through behavioral activation of one area and concomitant activation of another area with HD-tDCS.

Identifiants

pubmed: 38759354
pii: S1053-8119(24)00144-7
doi: 10.1016/j.neuroimage.2024.120649
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

120649

Informations de copyright

Copyright © 2024. Published by Elsevier Inc.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Camille Farcy (C)

Division of Neurorehabilitation, Department of Clinical Neurosciences, University Hospital of Geneva, Av. de Beau-Séjour 26, Geneva 1211, Switzerland.

Lea A S Chauvigné (LAS)

Division of Neurorehabilitation, Department of Clinical Neurosciences, University Hospital of Geneva, Av. de Beau-Séjour 26, Geneva 1211, Switzerland.

Marina Laganaro (M)

Neuropsycholinguistics Laboratory, University of Geneva, Geneva, Switzerland.

Marion Corre (M)

Division of Neurorehabilitation, Department of Clinical Neurosciences, University Hospital of Geneva, Av. de Beau-Séjour 26, Geneva 1211, Switzerland.

Radek Ptak (R)

Division of Neurorehabilitation, Department of Clinical Neurosciences, University Hospital of Geneva, Av. de Beau-Séjour 26, Geneva 1211, Switzerland.

Adrian G Guggisberg (AG)

Division of Neurorehabilitation, Department of Clinical Neurosciences, University Hospital of Geneva, Av. de Beau-Séjour 26, Geneva 1211, Switzerland; Universitäre Neurorehabilitation, Universitätsklinik für Neurologie, Inselspital, University Hospital of Berne, Berne 3010, Switzerland. Electronic address: aguggis@gmail.com.

Classifications MeSH