The relationship between electrophysiological and hemodynamic measures of neural activity varies across picture naming tasks : A multimodal magnetoencephalography-functional magnetic resonance imaging study
Mononen, T., Kujala, J., Liljeström, M., Leppäaho, E., Kaski, S., & Salmelin, R. (2022). The relationship between electrophysiological and hemodynamic measures of neural activity varies across picture naming tasks : A multimodal magnetoencephalography-functional magnetic resonance imaging study. Frontiers in Neuroscience, 16, Article 1019572. https://doi.org/10.3389/fnins.2022.1019572
Julkaistu sarjassa
Frontiers in NeuroscienceTekijät
Päivämäärä
2022Tekijänoikeudet
© 2022 Mononen, Kujala, Liljeström, Leppäaho, Kaski and Salmelin.
Different neuroimaging methods can yield different views of task-dependent neural engagement. Studies examining the relationship between electromagnetic and hemodynamic measures have revealed correlated patterns across brain regions but the role of the applied stimulation or experimental tasks in these correlation patterns is still poorly understood. Here, we evaluated the across-tasks variability of MEG-fMRI relationship using data recorded during three distinct naming tasks (naming objects and actions from action images, and objects from object images), from the same set of participants. Our results demonstrate that the MEG-fMRI correlation pattern varies according to the performed task, and that this variability shows distinct spectral profiles across brain regions. Notably, analysis of the MEG data alone did not reveal modulations across the examined tasks in the time-frequency windows emerging from the MEG-fMRI correlation analysis. Our results suggest that the electromagnetic-hemodynamic correlation could serve as a more sensitive proxy for task-dependent neural engagement in cognitive tasks than isolated within-modality measures.
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Frontiers Media SAISSN Hae Julkaisufoorumista
1662-4548Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/160132324
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This work was financially supported by the Academy of Finland (Finnish Center of Excellence in Computational Inference Research COIN and grants #292334, #294238 to SK; #255349, #315553 to RS; #257576 to JK; #286405 funding for TM), the Sigrid Jusélius Foundation (grant to RS), the Finnish Cultural Foundation (grant to ML), the Swedish Cultural Foundation in Finland (grant to ML), the Maud Kuistila Memorial Foundation (grant to ML), and Aalto Brain Center. ...Lisenssi
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