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High-quality multimodal MRI with simultaneous EEG using conductive ink and polymer-thick film nets

Nicholas G Cicero*, Nina E Fultz, Hongbae Jeong, Stephanie D Williams, Daniel Gomez, Beverly Setzer, Tracy Warbrick, Manfred Jaschke, Ravij Gupta, Michael Lev, Giorgio Bonmassar, Laura D Lewis

*Corresponding author for this work
3 Citations (Scopus)

Abstract

Objective. Combining magnetic resonance imaging (MRI) and electroencephalography (EEG) provides a powerful tool for investigating brain function at varying spatial and temporal scales. Simultaneous acquisition of both modalities can provide unique information that a single modality alone cannot reveal. However, current simultaneous EEG-fMRI studies are limited to a small set of MRI sequences due to the image quality and safety limitations of commercially available MR-conditional EEG nets. We tested whether the Inknet2, a high-resistance polymer thick film based EEG net that uses conductive ink, could enable the acquisition of a variety of MR image modalities with minimal artifacts by reducing the radiofrequency-shielding caused by traditional MR-conditional nets.Approach. We first performed simulations to model the effect of the EEG nets on the magnetic field and image quality. We then performed phantom scans to test image quality with a conventional copper EEG net, with the new Inknet2, and without any EEG net. Finally, we scanned five human subjects at 3 Tesla (3 T) and three human subjects at 7 Tesla (7 T) with and without the Inknet2 to assess structural and functional MRI image quality.Main results. Across these simulations, phantom scans, and human studies, the Inknet2 induced fewer artifacts than the conventional net and produced image quality similar to scans with no net present.Significance. Our results demonstrate that high-quality structural and functional multimodal imaging across a variety of MRI pulse sequences at both 3 T and 7 T is achievable with an EEG net made with conductive ink and polymer thick film technology.

Original languageEnglish
Article number066004
JournalJournal of Neural Engineering
Volume21
Issue number6
ISSN1741-2560
DOIs
Publication statusPublished - 5 Nov 2024

Keywords

  • MRI
  • artifacts
  • electroencephalogram
  • image quality
  • multimodal imaging

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