Data-driven model optimization for optically pumped magnetometer sensor arrays

Exploring the limits of MEG spatial resolution with multipolar expansions

The advent of scalp magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) may represent a step change in the field of human electrophysiology. Compared to cryogenic MEG based on superconducting quantum interference devices (SQUIDs, placed 2-4 cm above scalp), scalp MEG promises significantly higher spatial resolution imaging but it also comes with numerous challenges regarding how to optimally design OPM arrays. In this context, we sought to provide a systematic description of MEG spatial resolution as a function of the number of sensors (allowing comparison of low- vs. high-density MEG), sensor-to-brain distance (cryogenic SQUIDs vs. scalp OPM), sensor type (magnetometers vs. gradiometers; single- vs. multi-component sensors), and signal-to-noise ratio. To that aim, we present an analytical theory based on MEG multipolar expansions that enables, once supplemented with experimental input and simulations, quantitative assessment of the limits of MEG spatial resolution in terms of two qualitatively distinct regimes. In the regime of asymptotically high-density MEG, we provide a mathematically rigorous description of how magnetic field smoothness constraints spatial resolution to a slow, logarithmic divergence. In the opposite regime of low-density MEG, it is sensor density that constraints spatial resolution to a faster increase following a square-root law. The transition between these two regimes controls how MEG spatial resolution saturates as sensors approach sources of neural activity. This two-regime model of MEG spatial resolution integrates known observations (e.g., the difficulty of improving spatial resolution by increasing sensor density, the gain brought by moving sensors on scalp, or the usefulness of multi-component sensors) and gathers them under a unifying theoretical framework that highlights the underlying physics and reveals properties inaccessible to simulations. We propose that this framework may find useful applications to benchmark the design of future OPM-based scalp MEG systems.

Optimising the sensing volume of OPM sensors for MEG source reconstruction

Magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) has been hailed as the future of electrophysiological recordings from the human brain. In this work, we investigate how the dimensions of the sensing volume (the vapour cell) affect the performance of both a single OPM-MEG sensor and a multi-sensor OPM-MEG system. We consider a realistic noise model that accounts for background brain activity and residual noise. By using source reconstruction metrics such as localization accuracy and time-course reconstruction accuracy, we demonstrate that the best overall sensitivity and reconstruction accuracy are achieved with cells that are significantly longer and wider that those of the majority of current commercial OPM sensors. Our work provides useful tools to optimise the cell dimensions of OPM sensors in a wide range of environments.

OMMR: Co-registration toolbox of OPM-MEG and MRI

Magnetoencephalography (MEG) based on optically pumped magnetometers (OPM-MEG) has shown better flexibility in sensor configuration compared with the conventional superconducting quantum interference devices-based MEG system while being better suited for all-age groups. However, this flexibility presents challenges for the co-registration of MEG and magnetic resonance imaging (MRI), hindering adoption. This study presents a toolbox called OMMR, developed in Matlab, that facilitates the co-registration step for researchers and clinicians. OMMR integrates the co-registration methods of using the electromagnetic digitization system and two types of optical scanners (the structural-light and laser scanner). As the first open-source co-registration toolbox specifically for OPM-MEG, the toolbox aims to standardize the co-registration process and set the ground for future applications of OPM-MEG.

On-scalp magnetocorticography with optically pumped magnetometers: Simulated performance in resolving simultaneous sources

Currently, the gold standard for high-resolution mapping of cortical electrophysiological activity is invasive electrocorticography (ECoG), a procedure that carries with it the risk of serious morbidity and mortality. Due to these risks, the use of ECoG is largely limited to pre-surgical mapping in intractable epilepsy. Nevertheless, many seminal studies in neuroscience have utilized ECoG to explore domains such as visual perception, attention, auditory processing, and sensorimotor behavior. Studies such as these, occurring in patients with epilepsy rather than healthy controls, may lack generalizability, and are limited by the placement of the electrode arrays over the presumed seizure focus. This manuscript explores the use of optically pumped magnetometers (OPMs) to create a non-invasive alternative to ECoG, which we refer to as magnetocorticography. Because prior ECoG studies reveal that most cognitive processes are driven by multiple, simultaneous independent neuronal assemblies, we characterize the ability of a theoretical 56-channel dense OPM array to resolve simultaneous independent sources, and compare it to currently available SQUID devices, as well as OPM arrays with inter-sensor spacings more typical of other systems in development. Our evaluation of this theoretical system assesses many potential sources of error, including errors of sensor calibration and position. In addition, we investigate the influence of geometrical and anatomical factors on array performance. Our simulations reveal the potential of high-density, on-scalp OPM MEG devices to localize electrophysiological brain responses at unprecedented resolution for a non-invasive device.

Imaging somatosensory cortex responses measured by OPM-MEG: Variational free energy-based spatial smoothing estimation approach

In recent years, optically pumped magnetometer (OPM)-based magnetoencephalography (MEG) has shown potential for analyzing brain activity. It has a flexible sensor configuration and comparable sensitivity to conventional SQUID-MEG. We constructed a 32-channel OPM-MEG system and used it to measure cortical responses to median and ulnar nerve stimulations. Traditional magnetic source imaging methods tend to blur the spatial extent of sources. Accurate estimation of the spatial size of the source is important for studying the organization of brain somatotopy and for pre-surgical functional mapping. We proposed a new method called variational free energy-based spatial smoothing estimation (FESSE) to enhance the accuracy of mapping somatosensory cortex responses. A series of computer simulations based on the OPM-MEG showed better performance than the three types of competing methods under different levels of signal-to-noise ratios, source patch sizes, and co-registration errors. FESSE was then applied to the source imaging of the OPM-MEG experimental data.

Bridging M/EEG Source Imaging and Independent Component Analysis Frameworks Using Biologically Inspired Sparsity Priors

Electromagnetic source imaging (ESI) and independent component analysis (ICA) are two popular and apparently dissimilar frameworks for M/EEG analysis. This letter shows that the two frameworks can be linked by choosing biologically inspired source sparsity priors. We demonstrate that ESI carried out by the sparse Bayesian learning (SBL) algorithm yields source configurations composed of a few active regions that are also maximally independent from one another. In addition, we extend the standard SBL approach to source imaging in two important directions. First, we augment the generative model of M/EEG to include artifactual sources. Second, we modify SBL to allow for efficient model inversion with sequential data. We refer to this new algorithm as recursive SBL (RSBL), a source estimation filter with potential for online and offline imaging applications. We use simulated data to verify that RSBL can accurately estimate and demix cortical and artifactual sources under different noise conditions. Finally, we show that on real error-related EEG data, RSBL can yield single-trial source estimates in agreement with the experimental literature. Overall, by demonstrating that ESI can produce maximally independent sources while simultaneously localizing them in cortical space, we bridge the gap between the ESI and ICA frameworks for M/EEG analysis.

Optimal design of on-scalp electromagnetic sensor arrays for brain source localisation

Optically pumped magnetometers (OPMs) are quickly widening the scopes of noninvasive neurophysiological imaging. The possibility of placing these magnetic field sensors on the scalp allows not only to acquire signals from people in movement, but also to reduce the distance between the sensors and the brain, with a consequent gain in the signal‐to‐noise ratio. These advantages make the technique particularly attractive to characterise sources of brain activity in demanding populations, such as children and patients with epilepsy. However, the technology is currently in an early stage, presenting new design challenges around the optimal sensor arrangement and their complementarity with other techniques as electroencephalography (EEG). In this article, we present an optimal array design strategy focussed on minimising the brain source localisation error. The methodology is based on the Cramér‐Rao bound, which provides lower error bounds on the estimation of source parameters regardless of the algorithm used. We utilise this framework to compare whole head OPM arrays with commercially available electro/magnetoencephalography (E/MEG) systems for localising brain signal generators. In addition, we study the complementarity between EEG and OPM‐based MEG, and design optimal whole head systems based on OPMs only and a combination of OPMs and EEG electrodes for characterising deep and superficial sources alike. Finally, we show the usefulness of the approach to find the nearly optimal sensor positions minimising the estimation error bound in a given cortical region when a limited number of OPMs are available. This is of special interest for maximising the performance of small scale systems to ad hoc neurophysiological experiments, a common situation arising in most OPM labs.

The integration of social and neural synchrony: a case for ecologically valid research using MEG neuroimaging

The recent decade has seen a shift from artificial and environmentally deprived experiments in neuroscience to real-life studies on multiple brains in interaction, coordination and synchrony. In these new interpersonal synchrony experiments, there has been a growing trend to employ naturalistic social interactions to evaluate mechanisms underlying synchronous neuronal communication. Here, we emphasize the importance of integrating the assessment of neural synchrony with measurement of nonverbal behavioral synchrony as expressed in various social contexts: relaxed social interactions, planning a joint pleasurable activity, conflict discussion, invocation of trauma, or support giving and assess the integration of neural and behavioral synchrony across developmental stages and psychopathological conditions. We also showcase the advantages of magnetoencephalography neuroimaging as a promising tool for studying interactive neural synchrony and consider the challenge of ecological validity at the expense of experimental rigor. We review recent evidence of rhythmic information flow between brains in interaction and conclude with addressing state-of-the-art developments that may contribute to advance research on brain-to-brain coordination to the next level.

Data-driven model optimization for optically pumped magnetometer sensor arrays

Optically pumped magnetometers (OPMs) have reached sensitivity levels that make them viable portable alternatives to traditional superconducting technology for magnetoencephalography (MEG). OPMs do not require cryogenic cooling and can therefore be placed directly on the scalp surface. Unlike cryogenic systems, based on a well-characterised fixed arrays essentially linear in applied flux, OPM devices, based on different physical principles, present new modelling challenges. Here, we outline an empirical Bayesian framework that can be used to compare between and optimise sensor arrays. We perturb the sensor geometry (via simulation) and with analytic model comparison methods estimate the true sensor geometry. The width of these perturbation curves allows us to compare different MEG systems. We test this technique using simulated and real data from SQUID and OPM recordings using head-casts and scanner-casts. Finally, we show that given knowledge of underlying brain anatomy, it is possible to estimate the true sensor geometry from the OPM data themselves using a model comparison framework. This implies that the requirement for accurate knowledge of the sensor positions and orientations a priori may be relaxed. As this procedure uses the cortical manifold as spatial support there is no co-registration procedure or reliance on scalp landmarks.