Signal Space Separation Method for a Biomagnetic Sensor Array Arranged on a Flat Plane for Magnetocardiographic Applications: A Computer Simulation Study

Delays in latencies of median-nerve evoked magnetic fields in patients with succinic semialdehyde dehydrogenase deficiency

OBJECTIVE

Succinic semialdehyde dehydrogenase deficiency (SSADHD) is a genetic disorder resulting in abnormal regulation of γ-aminobutyric acid, lipid metabolism, and myelin biogenesis, leading to ataxia, seizures, and cognitive impairment. Since the myelin sheath is thinner in a murine model of SSADHD compared to a wild type, we hypothesized that this also holds for human brain. We tested whether the conduction velocity in the somatosensory pathway is accordingly delayed.

METHODS

Somatosensory evoked magnetic fields (SEF) produced by transcutaneous electrical stimulation of the median nerve were measured in 13 SSADHD patients, 11 healthy and 14 disease controls with focal epilepsy. The peak latencies of the initial four components (M1, M2, M3 and M4) were measured.

RESULTS

The SEF waveforms and scalp topographies were comparable across the groups. The latencies were statistically significantly longer in the SSADHD group compared to the two controls. We found these latencies for the SSADHD, healthy and disease controls respectively to be: M1: (21.9 ± 0.8 ms [mean ± standard error of the mean], 20.4 ± 0.6 ms, and 21.0 ± 0.4 ms) (p < 0.05); M2: (36.1 ± 1.0 ms, 33.1 ± 0.6 ms, and 32.1 ± 1.1 ms) (p < 0.005); M3: (62.5 ± 2.4 ms, 54.7 ± 2.0 ms, and 49.9 ± 1.8 ms) (p < 0.005); M4: (86.2 ± 2.3 ms, 78.8 ± 2.8 ms, and 73.5 ± 2.9 ms) (p < 0.005).

CONCLUSIONS

The SEF latencies are delayed in patients with SSADHD compared with healthy controls and disease controls.

SIGNIFICANCE

This is the first study that compares conduction velocities in the somatosensory pathway in SSADHD, an inherited disorder of GABA metabolism. The longer peak latency implying slower conduction velocity supports the hypothesis that myelin sheath thickness is decreased in SSADHD.

Optimization of Signal Space Separation for Optically Pumped Magnetometer in Magnetoencephalography

Magnetoencephalography (MEG) is a noninvasive functional neuroimaging modality but highly susceptible to environmental interference. Signal space separation (SSS) is a method for improving the SNR to separate the MEG signals from external interference. The origin and truncation values of SSS significantly affect the SSS performance. The origin value fluctuates with respect to the helmet array, and determining the truncation values using the traversal method is time-consuming; thus, this method is inappropriate for optically pumped magnetometer (OPM) systems with flexible array designs. Herein, an automatic optimization method for the SSS parameters is proposed. Virtual sources are set inside and outside the brain to simulate the signals of interest and interference, respectively, via forward model, with the sensor array as prior information. The objective function is determined as the error between the signals from simulated sources inside the brain and the SSS reconstructed signals; thus, the optimized parameters are solved inversely by minimizing the objective function. To validate the proposed method, a simulation analysis and MEG auditory-evoked experiments were conducted. For an OPM sensor array, this method can precisely determine the optimized origin and truncation values of the SSS simultaneously, and the auditory-evoked component, for example, N100, can be accurately located in the temporal cortex. The proposed optimization procedure outperforms the traditional method with regard to the computation time and accuracy, simplifying the SSS process in signal preprocessing and enhancing the performance of SSS denoising.

Comparison of DSSP and tSSS algorithms for removing artifacts from vagus nerve stimulators in magnetoencephalography data

OBJECTIVE

Large-amplitude artifacts from vagus nerve stimulator (VNS) implants for refractory epilepsy affect magnetoencephalography (MEG) recordings and are difficult to reject, resulting in unusable data from this important population of patients who are frequently evaluated for surgical treatment of epilepsy. Here we compare the performance of two artifact removal algorithms for MEG data: dual signal subspace projection (DSSP) and temporally extended signal space separation (tSSS).

APPROACH

Each algorithm's performance was first evaluated in simulations. We then tested the performance of each algorithm on resting-state MEG data from patients with VNS implants. We also examined how each algorithm improved source localization of somatosensory evoked fields in patients with VNS implants.

MAIN RESULTS

DSSP and tSSS algorithms have a similar ability to reject interference in both simulated and real MEG data if the origin location for tSSS is appropriately set. If the origin set for tSSS is inappropriate, the signal after tSSS can be distorted due to a mismatch between the internal region and the actual source space. Both DSSP and tSSS are able to remove large-amplitude artifacts from outside the brain. DSSP might be a better choice than tSSS when the choice of origin location for tSSS is difficult.

SIGNIFICANCE

Both DSSP and tSSS algorithms can recover distorted MEG recordings from people with intractable epilepsy and VNS implants, improving epileptic spike identification and source localization of both functional activity and epileptiform activity.