High-quality multimodal MRI with simultaneous EEG using conductive ink and polymer-thick film nets
<i>Objective</i>. 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.<i>Approach</i>. 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.<i>Main results</i>. 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.<i>Significance</i>. 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.
Authors
- Hongbae Jeong (ORCID: https://orcid.org/0000-0003-4908-2070)
- Nicholas G. Cicero (ORCID: https://orcid.org/0000-0001-6382-7353)
- Beverly Setzer (ORCID: https://orcid.org/0000-0002-2269-3288)
- Michael H. Lev (ORCID: https://orcid.org/0000-0003-0236-7319)
- Daniel Gómez (ORCID: https://orcid.org/0000-0001-9548-5781)
- Manfred Jaschke
- Nina E. Fultz (ORCID: https://orcid.org/0000-0003-2842-8247)
- Laura D. Lewis (ORCID: https://orcid.org/0000-0002-4003-0277)
- Tracy Warbrick (ORCID: https://orcid.org/0000-0001-8619-9409)
- Stephanie D. Williams (ORCID: https://orcid.org/0000-0003-1904-4874)
- Giorgio Bonmassar (ORCID: https://orcid.org/0000-0002-4694-9698)
- Rajiv Gupta (ORCID: https://orcid.org/0009-0009-3308-8962)
Institutions
- Boston University (US)
- Harvard University (US)
- Zeppelin Universität gemeinnützige GmbH (DE)
- Copenhagen University Hospital (DK)
- Rigshospitalet (DK)
- Massachusetts General Hospital (US)
- Boston Biomedical (United States) (US)
- Brain (Germany) (DE)
- Athinoula A. Martinos Center for Biomedical Imaging (US)
- Massachusetts Institute of Technology (US)
Publication Details
- Journal
- Journal of Neural Engineering
- Published
- 2024-10-17
- DOI
- https://doi.org/10.1088/1741-2552/ad8837
- Citations
- 4
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 1.37
Funders
- McKnight Foundation
- Massachusetts General Hospital
- National Institutes of Health
- National Institute on Aging
- National Institute of Neurological Disorders and Stroke
- National Institute of Biomedical Imaging and Bioengineering