Functional MRI of neuro-electro-magnetic oscillations: Statistical processing in the presence of system imperfections

Direct detection of magnetic fields elicited by neuronal activity using Magnetic Resonance Imaging (MRI) has been a long standing research goal, due to its potential to overcome limitations that are inherent to BOLD fMRI. The MRI signal can be sensitized to oscillating magnetic fields using spin-lock preparations. However, the susceptibility of spin-lock sequences to system imperfections has so far hindered their translational potential for in vivo experiments. Moreover, the sensitivity of the neuro-current MRI signal to the phase of neuro-electric oscillations generates high variance time courses that are not suited for analysis with traditional fMRI data processing techniques. In this work we study the impact of various MRI system imperfections on neuro-current MRI in simulations. Furthermore we propose Statistical Variance Mapping (SVarM) as a new data processing technique for generating activity maps from neuro-current MRI signal variance. Bloch simulations demonstrated substantial variations of signal intensity for a 400 Hz range of off-resonances and a 360° range of neuro-current oscillating phases. SVarM was tested on synthetic neuro-current data simulated with various degrees of system imperfections. The proposed technique was compared to the previously developed NEMO processing, which is based on mean analysis of time courses. Simulation results show improved resilience against Bo inhomogeneities with SVarM compared with NEMO processing (Dice coefficient of activation maps: 64.07% SVarM, 57.76% NEMO, \mathrm{p} < 0.02). Comparable or slightly improved robustness against \mathrm{B}_{1}^{+} inhomogeneities was observed as well as higher sensitivity in the absence of \mathrm{B}_{1}^{+} inhomogeneities (Dice score of activation maps: 58.34% SVarM, 49.70% NEMO, \mathrm{p} < 0.01). Finally, SVarM achieved better specificity for low SNR, resulting in activation maps with fewer false positive voxels (FP rate: 0.53% SVarM, 19.28% NEMO, \mathrm{p} < 0.01). These results underscore the importance of dedicated data processing methods and robust pulse sequences to facilitate the widespread use of direct neuro-current MRI in the presence of system imperfections.