Computational and Phantom-Based Feasibility Study of 3D dcNCI With Ultra-Low-Field MRI

The possibility to directly and non-invasively localize neuronal activities in the human brain, as for instance by performing neuronal current imaging (NCI) via magnetic resonance imaging (MRI), would be a breakthrough in neuroscience. In order to assess the feasibility of 3-dimensional (3D) NCI, comprehensive computational and physical phantom experiments using low-noise ultra-low-field (ULF) MRI technology were performed using two different source models within spherical phantoms. The source models, consisting of a single dipole and an extended dipole grid, were calibrated enabling the quantitative emulation of a long-lasting neuronal activity by the application of known current waveforms. The dcNCI experiments were also simulated by solving the Bloch equations using the calculated internal magnetic field distributions of the phantoms and idealized MRI fields. The simulations were then validated by physical phantom experiments using a moderate polarization field of 17 mT. A focal activity with an equivalent current dipole of about 150 nAm and a physiologically relevant depth of 35 mm could be resolved with an isotropic voxel size of 25 mm. The simulation tool enabled the optimization of the imaging parameters for sustained neuronal activities in order to predict maximum sensitivity.

Which Part of the Neuronal Current can be Determined by Electroencephalography?

This chapter considers the so-called four-shell model of the brain and assumes that a continuously distributed primary neuronal current is supported either within the cerebral cortex or only on the surface of the cortex, S c, and is normal to this surface. The authors show that in the former case, electroencephalogram recordings are affected only by the value of the irrotational part of the current denoted by the scalar function Ψ‎(τ‎) and by the gradient of Ψ‎(τ‎) on S c. An effective numerical procedure for reconstructing Ψ‎(τ‎) in the particular case of an ellipsoidal model is discussed. If the primary current is supported on S c, and it is normal to it, then it can be reconstructed uniquely.

Multi-Channel Whole-Head OPM-MEG: Helmet Design and a Comparison with a Conventional System

ABSTRACTMagnetoencephalography (MEG) is a powerful technique for functional neuroimaging, offering a non-invasive window on brain electrophysiology. MEG systems have traditionally been based on cryogenic sensors which detect the small extracranial magnetic fields generated by synchronised current in neuronal assemblies, however such systems have fundamental limitations. In recent years quantum-enabled devices, called optically-pumped magnetometers (OPMs), have promised to lift those restrictions, offering an adaptable, motion-robust MEG device, with improved data quality, at reduced cost. However, OPM-MEG remains a nascent technology, and whilst viable systems exist, most employ small numbers of sensors sited above targeted brain regions. Here, building on previous work, we construct a wearable OPM-MEG system with ‘whole-head’ coverage based upon commercially available OPMs, and test its capabilities to measure alpha, beta and gamma oscillations. We design two methods for OPM mounting; a flexible (EEG-like) cap and rigid (additively-manufactured) helmet. Whilst both designs allow for high quality data to be collected, we argue that the rigid helmet offers a more robust option with significant advantages for reconstruction of field data into 3D images of changes in neuronal current. Using repeat measurements in two participants, we show signal detection for our device to be highly robust. Moreover, via application of source-space modelling, we show that, despite having 5 times fewer sensors, our system exhibits comparable performance to an established cryogenic MEG device. While significant challenges still remain, these developments provide further evidence that OPM-MEG is likely to facilitate a step change for functional neuroimaging.HIGHLIGHTSA 49-channel whole-head OPM-MEG system is constructedSystem evaluated via repeat measurements of alpha, beta and gamma oscillationsTwo OPM-helmet designs are contrasted, a flexible (EEG-like) cap and a rigid helmetThe rigid helmet offers significant advantages for a viable OPM-MEG device49-channel OPM-MEG offers performance comparable to established cryogenic devices

A hybrid analytical–numerical algorithm for determining the neuronal current via electroencephalography

Specific mental processes are associated with brain activation of a unique form, which are, in turn, expressed via the generation of specific neuronal electric currents. Electroencephalography (EEG) is based on measurements on the scalp of the electric potential generated by the neuronal current flowing in the cortex. This specific form of EEG data has been employed for a plethora of medical applications, from sleep studies to diagnosing focal epilepsy. In recent years, there have been efforts to use EEG data for a more ambitious purpose, namely to determine the underlying neuronal current. Although it has been known since 1853, from the studies by Helmholtz, that the knowledge of the electric potential of the external surface of a conductor is insufficient for the determination of the electric current that gave rise to this potential, the important question of which part of the current can actually be determined from the knowledge of this potential remained open until work published in 1997, when it was shown that EEG provides information only about the irrotational part of the current, which will be denoted by Ψ ; moreover, an explicit formula was derived in the above work relating this part of the current, the measured electric potential, and a certain auxiliary function, v s , that depends on the geometry of the various compartments of the brain–head system and their conductivities. In the present paper: (i) Motivated by recent results which show that, in the case of ellipsoidal geometry, the assumption of the L 2 minimization of the current yields a unique solution, we derive an analogous analytic formula characterizing this minimization for arbitrary geometry. (ii) We show that the above auxiliary function can be computed numerically via a line integral from the values of a related function v s computed via OpenMEEG; moreover, we propose an alternative approach to computing the auxiliary function v s based on the construction of a certain surrogate model. (iii) By expanding Ψ in terms of an inverse multiquadric radial basis we implement the relevant formulae numerically. The above algorithm performs well for synthetic data; its implementation with real data only requires the knowledge of the coordinates of the positions where the given EEG data are obtained.

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

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.