RESUMEN
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.
Asunto(s)
Magnetometría/instrumentación , Modelos Teóricos , Algoritmos , Teorema de Bayes , Simulación por Computador , Estimulación Eléctrica , Diseño de Equipo , Potenciales Evocados Somatosensoriales/fisiología , Cabeza/anatomía & histología , Humanos , Funciones de Verosimilitud , Magnetoencefalografía/instrumentación , Magnetometría/métodos , Magnetometría/estadística & datos numéricos , Maniquíes , Cadenas de Markov , Nervio Mediano/fisiología , Dispositivos ÓpticosRESUMEN
MEG/EEG brain imaging has become an important tool in neuroimaging. Current techniques based in Bayesian approaches require an a-priori definition of patch locations on the cortical manifold. Too many patches results in a complex optimisation problem, too few an under sampling of the solution space. In this work random locations of the possible active regions of the brain are proposed to iteratively arrive at a solution. We use Bayesian model averaging to combine different possible solutions. The proposed methodology was tested with synthetic MEG datasets reducing the localisation error of the approaches based on fixed locations. Real data from a visual attention study was used for validation.