Abstract
Modern neuroimaging techniques enable non-invasive observation of ongoing neural processing, with magnetoencephalography (MEG) in particular providing direct measurement of neural activity with millisecond time resolution. However, accurately mapping measured MEG sensor readings onto the underlying source neural structures remains an active area of research. This so-called “inverse problem” is ill posed, and poses a challenge for source estimation that is often cited as a drawback limiting MEG data interpretation. However, anatomically constrained MEG localization estimates may be more accurate than commonly believed. Here we hypothesize that, by combining anatomically constrained inverse estimates across subjects, the spatial uncertainty of MEG source localization can be mitigated. Specifically, we argue that differences in subject brain geometry yield differences in point-spread functions, resulting in improved spatial localization across subjects. To test this, we use standard methods to combine subject anatomical MRI scans with coregistration information to obtain an accurate forward (physical) solution, modeling the MEG sensor data resulting from brain activity originating from different cortical locations. Using a linear minimum-norm inverse to localize this brain activity, we demonstrate that a substantial increase in the spatial accuracy of MEG source localization can result from combining data from subjects with differing brain geometry. This improvement may be enabled by an increase in the amount of available spatial information in MEG data as measurements from different subjects are combined. This approach becomes more important in the face of practical issues of coregistration errors and potential noise sources, where we observe even larger improvements in localization when combining data across subjects. Finally, we use a simple auditory N100(m) localization task to show how this effect can influence localization using a recorded neural dataset.
Highlights
Magnetoencephalography (MEG) is a physiological measurement technique that provides millisecond resolution of neural activity recorded from hundreds of sensors simultaneously
Anatomical MRI, MEG coregistration information, task-related and empty-room recordings were obtained from 20 subjects in order to examine how combining MEG inverse imaging estimates across subjects can affect localization of the neural activity
We simulated point-source activity from the cortical surface of each subject, with the location of activation conserved across subjects, and examined how precisely and accurately the inverse solution localized the average activity across subjects
Summary
Magnetoencephalography (MEG) is a physiological measurement technique that provides millisecond resolution of neural activity recorded from hundreds of sensors simultaneously. To localize MEG activity, additional constraints are necessary, where different methods are appropriate depending on underlying assumptions. Assuming that neural activity can be accounted for by a sparse constellation of focal sources or a set of distributed brain sources leads, respectively, to using minimum-current estimates (Gramfort et al, 2012) or minimum-norm estimates (Hämäläinen and Ilmoniemi, 1994). These issues result in ambiguity about which inverse method is appropriate, and what the underlying spatial resolution of MEG estimates is. With the increasing use of MEG for neuroimaging, addressing spatial specificity issues is especially important
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