The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Measuring MEG closer to the brain: Performance of on-scalp sensor arrays
Open accessPublished version of a preprint
Abstract
Optically-pumped magnetometers (OPMs) have recently reached sensitivity levels required for magnetoencephalography (MEG). OPMs do not need cryogenics and can thus be placed within millimetres from the scalp into an array that adapts to the individual head size and shape, thereby reducing the distance from cortical sources to the sensors. Here, we quantified the improvement in recording MEG with hypothetical on-scalp OPM arrays compared to a 306-channel state-of-the-art SQUID array (102 magnetometers and 204 planar gradiometers). We simulated OPM arrays that measured either normal (nOPM; 102 sensors), tangential (tOPM; 204 sensors), or all components (aOPM; 306 sensors) of the magnetic field. We built forward models based on magnetic resonance images of 10 adult heads; we employed a three-compartment boundary element model and distributed current dipoles evenly across the cortical mantle. Compared to the SQUID magnetometers, nOPM and tOPM yielded 7.5 and 5.3 times higher signal power, while the correlations between the field patterns of source dipoles were reduced by factors of 2.8 and 3.6, respectively. Values of the field-pattern correlations were similar across nOPM, tOPM and SQUID gradiometers. Volume currents reduced the signals of primary currents on average by 10%, 72% and 15% in nOPM, tOPM and SQUID magnetometers, respectively. The information capacities of the OPM arrays were clearly higher than that of the SQUID array. The dipole-localization accuracies of the arrays were similar while the minimum-norm-based point-spread functions were on average 2.4 and 2.5 times more spread for the SQUID array compared to nOPM and tOPM arrays, respectively.
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“Increased proximity to the scalp can improve sensitivity and spatial resolution beyond that which is achieved using a cryogenic MEG [26,27].”
From Naturalistic Hyperscanning with Wearable Magnetoencephalography (2023) · cites this paper as [27]
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Cites
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Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910 · cited 1×
“FreeSurfer software (Dale et al., 1999; Fischl et al., 1999a; Fischl, 2012) was used to segment the cortical mantles from individual MRIs.”
Fischl, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.021 · Cited by 9,526 · cited 1×
“FreeSurfer software (Dale et al., 1999; Fischl et al., 1999a; Fischl, 2012) was used to segment the cortical mantles from individual MRIs.”
Cortical Surface-Based Analysis
Fischl, B., Sereno, M. I., & Dale, A. M. (1999) · NeuroImage · doi:10.1006/nimg.1998.0396 · Cited by 6,147 · cited 2×
“FreeSurfer software (Dale et al., 1999; Fischl et al., 1999a; Fischl, 2012) was used to segment the cortical mantles from individual MRIs.”
Hämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J., & Lounasmaa, O. V. (1993) · Reviews of Modern Physics · doi:10.1103/revmodphys.65.413 · Cited by 4,684 · cited 2×
“2.3 Minimum-norm estimate Minimum-l2-norm estimator (Dale and Sereno, 1993; Hämäläinen and Ilmoniemi, 1994) can be used to estimate underlying sources from measured data.”
InfluentialA hybrid approach to the skull stripping problem in MRI
Ségonne, F., Dale, A. M., Busa, E., Glessner, M., Salat, D. H., Hahn, H., & Fischl, B. R. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.03.032 · Cited by 2,227 · cited 2×
“For each subject, we used the watershed algorithm (Ségonne et al., 2004) implemented in FreeSurfer and MNE software (Gramfort et al., 2014) to segment the brain, skull and scalp compartments.”
MNE software for processing MEG and EEG data
Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C. J., Parkkonen, L. T., & Hämäläinen, M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.10.027 · Cited by 2,035 · cited 1×
“, 2004) implemented in FreeSurfer and MNE software (Gramfort et al., 2014) to segment the brain, skull and scalp compartments.”
Self-citationDale, A. M., & Sereno, M. I. (1993) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1993.5.2.162 · Cited by 2,003 · cited 2×
“2.3 Minimum-norm estimate Minimum-l2-norm estimator (Dale and Sereno, 1993; Hämäläinen and Ilmoniemi, 1994) can be used to estimate underlying sources from measured data.”
InfluentialInterpreting magnetic fields of the brain: minimum norm estimates
Hämäläinen, M. S., & Ilmoniemi, R. J. (1994) · Medical & Biological Engineering & Computing · doi:10.1007/bf02512476 · Cited by 1,995 · cited 2×
“2.3 Minimum-norm estimate Minimum-l2-norm estimator (Dale and Sereno, 1993; Hämäläinen and Ilmoniemi, 1994) can be used to estimate underlying sources from measured data.”
Influential
Research works printed but not linked: 1
- Nenonen, J., Kajola, M., Simola, J., Ahonen, A., 2004. Total information of multichannel MEG sensor arrays. In: Proceedings of the 14th International Conference on Biomagnetism, Biomag, pp. 630–631.
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Sorted by citations
Brookes, M. J., Leggett, J., Rea, M., Hill, R. M., Holmes, N., Boto, E., & Bowtell, R. W. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2022.05.008 · Cited by 438
“Simulations [12,13] show that this effect provides a four- to fivefold signal enhancement in many cortical areas.”
BackgroundOptically pumped magnetometers: from quantum origins to multi-channel magnetoencephalography
Tierney, T. M., Holmes, N., Mellor, S., López, J. D., Roberts, G., Hill, R. M., Boto, E., Leggett, J., Shah, V., Brookes, M. J., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.063 · Cited by 400
“…advances in sensors, additional neuroscientific motivation for OPMs came from simulation studies examining issues such as improvements in SNR, spatial resolution and the requirements necessary for accurate source reconstruction (Boto et al., 2016a; Iivanainen et al., 2017a; Zetter et al., 2017).”
BackgroundMulti-Channel Whole-Head OPM-MEG: Helmet Design and a Comparison with a Conventional System
Hill, R. M., Boto, E., Rea, M., Holmes, N., Leggett, J., Coles, L. A., Papastavrou, M., Everton, S. K., Hunt, B. A. E., Sims, D., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116995 · Cited by 285
Ultrasensitive Magnetic Field Sensors for Biomedical Applications
Murzin, D., Mapps, D. J., Levada, K., Belyaev, V., Omelyanchik, A., Panina, L., & Rodionova, V. (2020) · Sensors · doi:10.3390/s20061569 · Cited by 277
On-scalp MEG system utilizing an actively shielded array of optically-pumped magnetometers
Iivanainen, J., Zetter, R., Grön, M., Hakkarainen, K., & Parkkonen, L. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.03.022 · Cited by 269
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Metadata
- Authors
- Iivanainen, J., Stenroos, M., & Parkkonen, L.
- Affiliations
- Aalto University (FI)
- Corresponding author
- Iivanainen, Joonas
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 147
- Pages
- 542-553
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC5432137
- OpenAlex
- W2949327983
- Cited by
- 296
- References
- 47
- Citations by year
- 2026: 28 · 2025: 49 · 2024: 39 · 2023: 40 · 2022: 36 · 2021: 30 · 2020: 25 · 2019: 32 · 2018: 10 · 2017: 7
- FWCI
- 7.72
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Aaltodoc (Aalto University) (submittedVersion, cc-by-nc-nd)
- Funders
- National Institute of Neurological Disorders and Stroke
- Seventh Framework Programme (600730)
- National Institutes of Health (R01NS094604)
- Topics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Atomic and Subatomic Physics Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Magnetic Field Sensors Techniques
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- magnetoencephalography, optically pumped magnetometers, SQUID magnetometer, planar gradiometer, forward modeling, boundary element model, dipole source localization, point spread function, information capacity, volume currents, signal-to-noise ratio
- MeSH terms
- Adult, Brain, Female, Humans, Male, Models, Theoretical, Scalp, Signal Processing, Computer-Assisted, Magnetoencephalography
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