Sofie S. Meyer

A new generation of magnetoencephalography: room temperature measurements using optically-pumped magnetometers

ABSTRACT Advances in the field of quantum sensing mean that magnetic field sensors, operating at room temperature, are now able to achieve sensitivity similar to that of cryogenically cooled devices (SQUIDs). This means that room temperature magnetoencephalography (MEG), with a greatly increased flexibility of sensor placement can now be considered. Further, these new sensors can be placed directly on the scalp surface giving, theoretically, a large increase in the magnitude of the measured signal. Here, we present recordings made using a single optically‐pumped magnetometer (OPM) in combination with a 3D‐printed head‐cast designed to accurately locate and orient the sensor relative to brain anatomy. Since our OPM is configured as a magnetometer it is highly sensitive to environmental interference. However, we show that this problem can be ameliorated via the use of simultaneous reference sensor recordings. Using median nerve stimulation, we show that the OPM can detect both evoked (phase‐locked) and induced (non‐phase‐locked oscillatory) changes when placed over sensory cortex, with signals ˜4 times larger than equivalent SQUID measurements. Using source modelling, we show that our system allows localisation of the evoked response to somatosensory cortex. Further, source‐space modelling shows that, with 13 sequential OPM measurements, source‐space signal‐to‐noise ratio (SNR) is comparable to that from a 271‐channel SQUID system. Our results highlight the opportunity presented by OPMs to generate uncooled, potentially low‐cost, high SNR MEG systems.

Flexible head-casts for high spatial precision MEG

Highlights • We propose a method for constructing flexible head-casts to stabilize the head during MEG scanning.• Co-registration error is minimized by using MRI images to pre-define fiducial coil locations.• Within- and between-session movement is <0.25 and <1 mm respectively.• This enables high reproducibility of source level results.

On the Potential of a New Generation of Magnetometers for MEG: A Beamformer Simulation Study

Magnetoencephalography (MEG) is a sophisticated tool which yields rich information on the spatial, spectral and temporal signatures of human brain function. Despite unique potential, MEG is limited by a low signal-to-noise ratio (SNR) which is caused by both the inherently small magnetic fields generated by the brain, and the scalp-to-sensor distance. The latter is limited in current systems due to a requirement for pickup coils to be cryogenically cooled. Recent work suggests that optically-pumped magnetometers (OPMs) might be a viable alternative to superconducting detectors for MEG measurement. They have the advantage that sensors can be brought to within ~4 mm of the scalp, thus offering increased sensitivity. Here, using simulations, we quantify the advantages of hypothetical OPM systems in terms of sensitivity, reconstruction accuracy and spatial resolution. Our results show that a multi-channel whole-head OPM system offers (on average) a fivefold improvement in sensitivity for an adult brain, as well as clear improvements in reconstruction accuracy and spatial resolution. However, we also show that such improvements depend critically on accurate forward models; indeed, the reconstruction accuracy of our simulated OPM system only outperformed that of a simulated superconducting system in cases where forward field error was less than 5%. Overall, our results imply that the realisation of a viable whole-head multi-channel OPM system could generate a step change in the utility of MEG as a means to assess brain electrophysiological activity in health and disease. However in practice, this will require both improved hardware and modelling algorithms.

Moving magnetoencephalography towards real-world applications with a wearable system

Imaging human brain function with techniques such as magnetoencephalography typically requires a subject to perform tasks while their head remains still within a restrictive scanner. This artificial environment makes the technique inaccessible to many people, and limits the experimental questions that can be addressed. For example, it has been difficult to apply neuroimaging to investigation of the neural substrates of cognitive development in babies and children, or to study processes in adults that require unconstrained head movement (such as spatial navigation). Here we describe a magnetoencephalography system that can be worn like a helmet, allowing free and natural movement during scanning. This is possible owing to the integration of quantum sensors, which do not rely on superconducting technology, with a system for nulling background magnetic fields. We demonstrate human electrophysiological measurement at millisecond resolution while subjects make natural movements, including head nodding, stretching, drinking and playing a ball game. Our results compare well to those of the current state-of-the-art, even when subjects make large head movements. The system opens up new possibilities for scanning any subject or patient group, with myriad applications such as characterization of the neurodevelopmental connectome, imaging subjects moving naturally in a virtual environment and investigating the pathophysiology of movement disorders.

Using generative models to make probabilistic statements about hippocampal engagement in MEG.

ABSTRACT Magnetoencephalography (MEG) enables non‐invasive real time characterization of brain activity. However, convincing demonstrations of signal contributions from deeper sources such as the hippocampus remain controversial and are made difficult by its depth, structural complexity and proximity to neocortex. Here, we demonstrate a method for quantifying hippocampal engagement probabilistically using simulated hippocampal activity and realistic anatomical and electromagnetic source modelling. We construct two generative models, one which supports neuronal current flow on the cortical surface, and one which supports neuronal current flow on both the cortical and hippocampal surface. Using Bayesian model comparison, we then infer which of the two models provides a more likely explanation of the dataset at hand. We also carry out a set of control experiments to rule out bias, including simulating medial temporal lobe sources to assess the risk of falsely positive results, and adding different types of displacements to the hippocampal portion of the mesh to test for anatomical specificity of the results. In addition, we test the robustness of this inference by adding co‐registration error and sensor level noise. We find that the model comparison framework is sensitive to hippocampal activity when co‐registration error is <3 mm and the sensor‐level signal‐to‐noise ratio (SNR) is >−20 dB. These levels of co‐registration error and SNR can now be achieved empirically using recently developed subject‐specific head‐casts.

Medial prefrontal-medial temporal theta phase coupling in dynamic spatial imagery

Hippocampal–medial prefrontal interactions are thought to play a crucial role in mental simulation. Notably, the frontal midline/medial pFC (mPFC) theta rhythm in humans has been linked to introspective thought and working memory. In parallel, theta rhythms have been proposed to coordinate processing in the medial temporal cortex, retrosplenial cortex (RSc), and parietal cortex during the movement of viewpoint in imagery, extending their association with physical movement in rodent models. Here, we used noninvasive whole-head MEG to investigate theta oscillatory power and phase-locking during the 18-sec postencoding delay period of a spatial working memory task, in which participants imagined previously learned object sequences either on a blank background (object maintenance), from a first-person viewpoint in a scene (static imagery), or moving along a path past the objects (dynamic imagery). We found increases in 4- to 7-Hz theta power in mPFC when comparing the delay period with a preencoding baseline. We then examined whether the mPFC theta rhythm was phase-coupled with ongoing theta oscillations elsewhere in the brain. The same mPFC region showed significantly higher theta phase coupling with the posterior medial temporal lobe/RSc for dynamic imagery versus either object maintenance or static imagery. mPFC theta phase coupling was not observed with any other brain region. These results implicate oscillatory coupling between mPFC and medial temporal lobe/RSc theta rhythms in the dynamic mental exploration of imagined scenes.

Non-invasive laminar inference with MEG: Comparison of methods and source inversion algorithms

&NA; Magnetoencephalography (MEG) is a direct measure of neuronal current flow; its anatomical resolution is therefore not constrained by physiology but rather by data quality and the models used to explain these data. Recent simulation work has shown that it is possible to distinguish between signals arising in the deep and superficial cortical laminae given accurate knowledge of these surfaces with respect to the MEG sensors. This previous work has focused around a single inversion scheme (multiple sparse priors) and a single global parametric fit metric (free energy). In this paper we use several different source inversion algorithms and both local and global, as well as parametric and non‐parametric fit metrics in order to demonstrate the robustness of the discrimination between layers. We find that only algorithms with some sparsity constraint can successfully be used to make laminar discrimination. Importantly, local t‐statistics, global cross‐validation and free energy all provide robust and mutually corroborating metrics of fit. We show that discrimination accuracy is affected by patch size estimates, cortical surface features, and lead field strength, which suggests several possible future improvements to this technique. This study demonstrates the possibility of determining the laminar origin of MEG sensor activity, and thus directly testing theories of human cognition that involve laminar‐ and frequency‐specific mechanisms. This possibility can now be achieved using recent developments in high precision MEG, most notably the use of subject‐specific head‐casts, which allow for significant increases in data quality and therefore anatomically precise MEG recordings. Section: Analysis methods. Classifications: Source localization: inverse problem; Source localization: other. HighlightsLaminar inferences can be made with MEG using both local and global fit metrics.Source inversion algorithms with sparsity constraints performed best.Classification is affected by patch size estimates, anatomy, and lead field strength.

Neural competitive queuing of ordinal structure underlies skilled sequential action

The fluent retrieval and production of movement sequences is essential for a variety of daily activities such as speech, tool-use, musical and athletic performance, but the neural mechanisms underlying sequence planning remain elusive. Here, participants learned sequences of finger presses with different timings and different finger orders, and reproduced them in a magneto-encephalography (MEG) scanner. We classified the MEG patterns immediately preceding each press in the sequence, and examined their dynamics over the production of the whole sequence. Our results confirm a role for the ‘competitive queuing’ of upcoming action representations in the production of learned motor sequences, extending previous computational and non-human primate recording studies to non-invasive measures in humans. In addition, we show that competitive queuing does not simply reflect specific motor actions, but representations of higher-level sequential order that generalise across different motor sequences. Finally, we show that the quality of competitive queuing predicts participants’ production accuracy, and originates from parahippocampal and cerebellar sources. These results suggest that the brain learns and produces multiple behavioural sequences by flexibly combining representations of specific actions with more abstract, parallel representations of sequential structure.

Cognitive neuroscience using wearable magnetometer arrays: Non-invasive assessment of language function

ABSTRACT Recent work has demonstrated that Optically Pumped Magnetometers (OPMs) can be utilised to create a wearable Magnetoencephalography (MEG) system that is motion robust. In this study, we use this system to map eloquent cortex using a clinically validated language lateralisation paradigm (covert verb generation: 120 trials, ˜10min total duration) in healthy adults (n=3). We show that it is possible to lateralise and localise language function on a case by case basis using this system. Specifically, we show that at a sensor and source level we can reliably detect a lateralising beta band (15–30Hz) desynchronization in all subjects. This is the first study of human cognition using OPMs and not only highlights this technologys utility as tool for (developmental) cognitive neuroscience but also its potential to contribute to surgical planning via mapping of eloquent cortex, especially in young children. HIGHLIGHTSFirst cognitive neuroscience experiment using optically pumped magnetometers.Language lateralisation is feasible with optically pumped magnetometers.Robust within‐subject effects at sensor and source level.

Quantifying the performance of MEG source reconstruction using resting state data

ABSTRACT In magnetoencephalography (MEG) research there are a variety of inversion methods to transform sensor data into estimates of brain activity. Each new inversion scheme is generally justified against a specific simulated or task scenario. The choice of this scenario will however have a large impact on how well the scheme performs. We describe a method with minimal selection bias to quantify algorithm performance using human resting state data. These recordings provide a generic, heterogeneous, and plentiful functional substrate against which to test different MEG recording and reconstruction approaches. We used a Hidden Markov model to spatio‐temporally partition data into self‐similar dynamic states. To test the anatomical precision that could be achieved, we then inverted these data onto libraries of systematically distorted subject‐specific cortical meshes and compared the quality of the fit using cross validation and a Free energy metric. This revealed which inversion scheme was able to identify the least distorted (most accurate) anatomical models, and allowed us to quantify an upper bound on the mean anatomical distortion accordingly. We used two resting state datasets, one recorded with head‐casts and one without. In the head‐cast data, the Empirical Bayesian Beamformer (EBB) algorithm showed the best mean anatomical discrimination (3.7mm) compared with Minimum Norm/LORETA (6.0mm) and Multiple Sparse Priors (9.4mm). This pattern was replicated in the second (conventional dataset) although with a marginally poorer (non‐significant) prediction of the missing (cross‐validated) data. Our findings suggest that the abundant resting state data now commonly available could be used to refine and validate MEG source reconstruction methods and/or recording paradigms. HIGHLIGHTSResting state data provides an unbiased dataset for comparing MEG source estimates.We use a hidden Markov model to break the data into stationary segments.We reconstruct these data onto a range of distorted cortical surfaces.The sensitivity of any algorithm to spatial distortion is quantified in millimetres.