| 1. |
- Andersen, L. M., et al.
(författare)
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On-scalp MEG SQUIDs are sensitive to early somatosensory activity unseen by conventional MEG
- 2020
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Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 221
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Tidskriftsartikel (refereegranskat)abstract
- Magnetoencephalography (MEG) has a unique capacity to resolve the spatio-temporal development of brain activity from non-invasive measurements. Conventional MEG, however, relies on sensors that sample from a distance (20–40 mm) to the head due to thermal insulation requirements (the MEG sensors function at 4 K in a helmet). A gain in signal strength and spatial resolution may be achieved if sensors are moved closer to the head. Here, we report a study comparing measurements from a seven-channel on-scalp SQUID MEG system to those from a conventional (in-helmet) SQUID MEG system. We compared the spatio-temporal resolution between on-scalp and conventional MEG by comparing the discrimination accuracy for neural activity patterns resulting from stimulating five different phalanges of the right hand. Because of proximity and sensor density differences between on-scalp and conventional MEG, we hypothesized that on-scalp MEG would allow for a more high-resolved assessment of these activity patterns, and therefore also a better classification performance in discriminating between neural activations from the different phalanges. We observed that on-scalp MEG provided better classification performance during an early post-stimulus period (10–20 ms). This corresponded to the electroencephalographic (EEG) component P16/N16 and was an unexpected observation as this component is usually not observed in conventional MEG. This finding shows that on-scalp MEG enables a richer registration of the cortical signal, indicating a sensitivity to what are potentially sources in the thalamo-cortical radiation. We had originally expected that on-scalp MEG would provide better classification accuracy based on activity in proximity to the P60m component compared to conventional MEG. This component indeed allowed for the best classification performance for both MEG systems (60–75%, chance 50%). However, we did not find that on-scalp MEG allowed for better classification than conventional MEG at this latency. We suggest that this absence of differences is due to the limited sensor coverage in the recording, in combination with our strategy for positioning the on-scalp MEG sensors. We show how the current sensor coverage may have limited our chances to register the necessary between-phalange source field dissimilarities for fair hypothesis testing, an approach we otherwise believe to be useful for future benchmarking measurements. © 2020 The Authors
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| 2. |
- Pfeiffer, Christoph, 1989, et al.
(författare)
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On-scalp MEG sensor localization using magnetic dipole-like coils: A method for highly accurate co-registration
- 2020
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Ingår i: Neuroimage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 212
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Tidskriftsartikel (refereegranskat)abstract
- Source modelling in magnetoencephalography (MEG) requires precise co-registration of the sensor array and the anatomical structure of the measured individual's head. In conventional MEG, the positions and orientations of the sensors relative to each other are fixed and known beforehand, requiring only localization of the head relative to the sensor array. Since the sensors in on-scalp MEG are positioned on the scalp, locations of the individual sensors depend on the subject's head shape and size. The positions and orientations of on-scalp sensors must therefore be measured a every recording. This can be achieved by inverting conventional head localization, localizing the sensors relative to the head - rather than the other way around. In this study we present a practical method for localizing sensors using magnetic dipole-like coils attached to the subject's head. We implement and evaluate the method in a set of on-scalp MEG recordings using a 7-channel on-scalp MEG system based on high critical temperature superconducting quantum interference devices (high-T-c SQUIDs). The method allows individually localizing the sensor positions, orientations, and responsivities with high accuracy using only a short averaging time (<= 2 mm, < 3 degrees and < 3%, respectively, with 1-s averaging), enabling continuous sensor localization. Calibrating and jointly localizing the sensor array can further improve the accuracy of position and orientation (< 1 mm and < 1 degrees, respectively, with 1-s coil recordings). We demonstrate source localization of on-scalp recorded somatosensory evoked activity based on coregistration with our method. Equivalent current dipole fits of the evoked responses corresponded well (within 4.2 mm) with those based on a commercial, whole-head MEG system.
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| 3. |
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| 4. |
- Sidén, Per, et al.
(författare)
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Fast Bayesian whole-brain fMRI analysis with spatial 3D priors
- 2017
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Ingår i: NeuroImage. - : Elsevier. - 1053-8119 .- 1095-9572. ; 146, s. 211-225
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Tidskriftsartikel (refereegranskat)abstract
- Spatial whole-brain Bayesian modeling of task-related functional magnetic resonance imaging (fMRI) is a great computational challenge. Most of the currently proposed methods therefore do inference in subregions of the brain separately or do approximate inference without comparison to the true posterior distribution. A popular such method, which is now the standard method for Bayesian single subject analysis in the SPM software, is introduced in Penny et al. (2005b). The method processes the data slice-by-slice and uses an approximate variational Bayes (VB) estimation algorithm that enforces posterior independence between activity coefficients in different voxels. We introduce a fast and practical Markov chain Monte Carlo (MCMC) scheme for exact inference in the same model, both slice-wise and for the whole brain using a 3D prior on activity coefficients. The algorithm exploits sparsity and uses modern techniques for efficient sampling from high-dimensional Gaussian distributions, leading to speed-ups without which MCMC would not be a practical option. Using MCMC, we are for the first time able to evaluate the approximate VB posterior against the exact MCMC posterior, and show that VB can lead to spurious activation. In addition, we develop an improved VB method that drops the assumption of independent voxels a posteriori. This algorithm is shown to be much faster than both MCMC and the original VB for large datasets, with negligible error compared to the MCMC posterior.
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