Abstract
In this paper, we study the performance of a source montage corresponding to 29 brain regions reconstructed from whole-head magnetoencephalographic (MEG) recordings, with the aim of facilitating the review of MEG data containing epileptiform discharges. Test data were obtained by superposing simulated signals from 100-nAm dipolar sources to a resting state MEG recording from a healthy subject. Simulated sources were placed systematically to different cortical locations for defining the optimal regularization for the source montage reconstruction and for assessing the detectability of the source activity from the 29-channel MEG source montage. The signal-to-noise ratio (SNR), computed for each source from the sensor-level and source-montage signals, was used as the evaluation parameter. Without regularization, the SNR from the simulated sources was larger in the sensor-level signals than in the source montage reconstructions. Setting the regularization to 2% increased the source montage SNR to the same level as the sensor-level SNR, improving the detectability of the simulated events from the source montage reconstruction. Sources producing a SNR of at least 15 dB were visually detectable from the source-montage signals. Such sources are located closer than about 75 mm from the MEG sensors, in practice covering all areas in the grey matter. The 29-channel source montage creates more focal signals compared to the sensor space and can significantly shorten the detection time of epileptiform MEG discharges for focus localization.
Highlights
Magnetoencephalography (MEG) is routinely used for recording epileptiform discharges and is used in many hospitals for the presurgical evaluation of patients with epilepsy [1,2,3,4,5,6]
Source montages have been proposed for transforming the sensor-level MEG and electroencephalography (EEG) signals into virtual source-space signals representing the neural activity in different brain regions
When using adapted regularization of 2%, the signal-to-noise ratio (SNR) of source montages increased to values comparable with the sensor-level values and the Ndt2 counts became closer to the Ndt1 values in Simulation 3 (Table 5)
Summary
Magnetoencephalography (MEG) is routinely used for recording epileptiform discharges and is used in many hospitals for the presurgical evaluation of patients with epilepsy [1,2,3,4,5,6]. Source montages have been proposed for transforming the sensor-level MEG and electroencephalography (EEG) signals into virtual source-space signals representing the neural activity in different brain regions. Due to a substantially lower number of channels, the source montages facilitate much faster identification and evaluation of epileptiform activity than the sensor-level signals [6,7,8,9]. A restricted inverse problem is, solvable, for example when the neural activity is estimated in terms of a limited number of current-dipole sources at pre-determined locations [9,10,11]. Source montages utilize a special spatial filter for converting the MEG and EEG sensor-level signals into the waveforms of standard regional sources in the cortex [8,9]. A regional source is fixed to the local brain structure by assuming one equivalent location in the depth of the gyrus or cortical sub-region, having two tangential components for MEG in a spherical head model, or three orthogonal components for the corresponding EEG data [8]
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