Virtual MEG Helmet: Computer Simulation of an Approach to Neuromagnetic Field Sampling

Abstract

Head movements during an MEG recording are commonly considered an obstacle. In this computer simulation study, we introduce an approach, the virtual MEG helmet (VMH), which employs the head movements for data quality improvement. With a VMH, a denser MEG helmet is constructed by adding new sensors corresponding to different head positions. Based on the Shannon's theory of communication, we calculated the total information as a figure of merit for comparing the actual 306-sensor Elekta Neuromag helmet to several types of the VMH. As source models, we used simulated randomly distributed source current (RDSC), simulated auditory and somatosensory evoked fields. Using the RDSC model with the simulation of 360 recorded events, the total information (bits/sample) was 989 for the most informative single head position and up to 1272 for the VMH (addition of 28.6%). Using simulated AEFs, the additional contribution of a VMH was 12.6% and using simulated SEF only 1.1%. For the distributed and bilateral sources, a VMH can provide a more informative sampling of the neuromagnetic field during the same recording time than measuring the MEG from one head position. VMH can, in some situations, improve source localization of the neuromagnetic fields related to the normal and pathological brain activity. This should be investigated further employing real MEG recordings.

Flexible head-casts for high spatial precision MEG

“…Although some progress has been made in minimizing co-registration error (Hironaga et al, 2014, Koessler et al, 2011, Nunez and Silberstein, 2000, Whalen et al, 2008), for example by stabilizing the head during recording (Adjamian et al, 2004, Singh et al, 1997), or compensating for movements both during and after recording (Medvedovsky et al, 2015, Medvedovsky et al, 2007, Nenonen et al, 2012, Stolk et al, 2013, Uutela et al, 2001), implementation problems have remained. The sources of residual error include misalignment of surfaces, amplification of small placement errors at the front of the head to large errors at the back of the head, and/or reliance on invariance in fiducial placement within and across experimenters and subjects (Adjamian et al, 2004).…”

Interictal and Ictal MEG in presurgical evaluation for epilepsy surgery

“…For example, prolonged video-MEG/EEG increase the probabilities to record and identify seizures [51]. Movement compensation [26,52] and the projection of recorded data to a standard virtual MEG helmet [53] enable the correction of (limited) patient movement during a seizure and increases the percentage of usable MEG recordings. Analytically, the application of STFT [27,28,31] for identification of seizure onsets, and the use of distributed source localization approaches [32,34], especially frequency-based methods [31], seem to improve the localization accuracy in comparison to ECD approaches.…”

Infant brain imaging using magnetoencephalography: Challenges, solutions, and best practices

“…Interestingly, from a physics point of view, head movements can be equivalently considered as movements of the sensor array around a static head. Provided that the distance between the head and the sensors remains reasonably short, this can actually lead to more comprehensive spatial sampling of the field, leading to increased information of the underlying neural currents (Medvedovsky et al, 2016 ). Thus, head movements do not necessarily deteriorate signal quality as long as their effects are taken into account mathematically.…”