Abstract
In recent years, the source localization technique of magnetoencephalography (MEG) has played a prominent role in cognitive neuroscience and in the diagnosis and treatment of neurological and psychological disorders. However, locating deep brain activities such as in the mesial temporal structures, especially in preoperative evaluation of epilepsy patients, may be more challenging. In this work we have proposed a modified beamforming approach for finding deep sources. First, an iterative spatiotemporal signal decomposition was employed for reconstructing the sensor arrays, which could characterize the intrinsic discriminant features for interpreting sensor signals. Next, a sensor covariance matrix was estimated under the new reconstructed space. Then, a well-known vector beamforming approach, which was a linearly constraint minimum variance (LCMV) approach, was applied to compute the solution for the inverse problem. It can be shown that the proposed source localization approach can give better localization accuracy than two other commonly-used beamforming methods (LCMV, MUSIC) in simulated MEG measurements generated with deep sources. Further, we applied the proposed approach to real MEG data recorded from ten patients with medically-refractory mesial temporal lobe epilepsy (mTLE) for finding epileptogenic zone(s), and there was a good agreement between those findings by the proposed approach and the clinical comprehensive results.
Highlights
MEG is a functional neuroimaging technique that captures neural activity with high spatiotemporal resolution and minor signal deterioration from the skull and scalp [1,2,3,4]
Many studies have focused their attention on detecting surface sources in MEG measurements, and have found the source localization methods are effective and helpful for finding the epileptogenic zone of neocortical epilepsy [5,38,39,40,41]
The ability of detecting deep sources such as those in the mesial temporal lobe remains in question [7,8,9,42]
Summary
MEG is a functional neuroimaging technique that captures neural activity with high spatiotemporal resolution and minor signal deterioration from the skull and scalp [1,2,3,4]. Since different source activities can generate an identical magnetic field distribution at the MEG sensor arrays, source localization techniques become an essential step for finding real sources by modeling the inverse solution in one assumption. Many studies have shown that MEG source localization methods seem to be effective and helpful for detecting surface sources, especially for finding the epileptogenic zone of neocortical epilepsy [5,6]. The magnetometer has been considered for spike detection in patients with mesial temporal epileptic focus [10], the clinical utility of MEG has not been recognized by consensus in preoperative evaluation with epileptogenic foci in deep regions [11]. A technical challenge is whether signals from deep sources could be well detected and correctly located using a MEG device and source localization algorithms
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