Neurophysiological trajectories in Alzheimer's disease progression.

Alzheimer's disease (AD) is characterized by the accumulation of amyloid-ß and misfolded tau proteins causing synaptic dysfunction, and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relationship to neurodegeneration and cognitive decline are unknown. Here, we deployed robust event-based sequencing models (EBMs) to investigate the trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. The increases in neural synchrony in the delta-theta band and the decreases in the alpha and beta bands showed progressive changes throughout the stages of the EBM. Decreases in alpha and beta band synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. The long-range synchrony effects were greater than the local synchrony, indicating a greater sensitivity of connectivity metrics involving multiple regions of the brain. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.

Assessing ulnar neuropathy at the elbow using magnetoneurography.

OBJECTIVE: To measure neuromagnetic fields of ulnar neuropathy patients at the elbow after electrical stimulation and evaluate ulnar nerve function at the elbow with high spatial resolution. METHODS: A superconducting quantum interference device magnetometer system recorded neuromagnetic fields of the ulnar nerve at the elbow after electrical stimulation at the wrist in 16 limbs of 16 healthy volunteers and 21 limbs of 20 patients with ulnar neuropathy at the elbow. After artifact removal, neuromagnetic field signals were processed into current distributions, which were superimposed onto X-ray images for visualization. RESULTS: Based on the results in healthy volunteers, conduction velocity of 30 m/s or 50% attenuation in current amplitude was set as the reference value for conduction disturbance. Of the 21 patient limbs, 15 were measurable and lesion sites were detected, whereas 6 limbs were unmeasurable due to weak neuromagnetic field signals. Seven limbs were deemed normal by nerve conduction study, but 5 showed conduction disturbances on magnetoneurography. CONCLUSIONS: Measuring the magnetic field after nerve stimulation enabled visualization of neurophysiological activity in patients with ulnar neuropathy at the elbow and evaluation of conduction disturbances. SIGNIFICANCE: Magnetoneurography may be useful for assessing lesion sites in patients with ulnar neuropathy at the elbow.

Neurophysiological trajectories in Alzheimer's disease progression.

Alzheimer's disease (AD) is characterized by the accumulation of amyloid-ß and misfolded tau proteins causing synaptic dysfunction, and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relationship to neurodegeneration and cognitive decline are unknown. Here, we deployed robust event-based sequencing models (EBMs) to investigate the trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. The increases in neural synchrony in the delta-theta band and the decreases in the alpha and beta bands showed progressive changes throughout the stages of the EBM. Decreases in alpha and beta band synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. The long-range synchrony effects were greater than the local synchrony, indicating a greater sensitivity of connectivity metrics involving multiple regions of the brain. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.

Bayesian Adaptive Beamformer for Robust Electromagnetic Brain Imaging of Correlated Sources in High Spatial Resolution.

Reconstructing complex brain source activity at a high spatiotemporal resolution from magnetoencephalography (MEG) or electroencephalography (EEG) remains a challenging problem. Adaptive beamformers are routinely deployed for this imaging domain using the sample data covariance. However adaptive beamformers have long been hindered by 1) high degree of correlation between multiple brain sources, and 2) interference and noise embedded in sensor measurements. This study develops a novel framework for minimum variance adaptive beamformers that uses a model data covariance learned from data using a sparse Bayesian learning algorithm (SBL-BF). The learned model data covariance effectively removes influence from correlated brain sources and is robust to noise and interference without the need for baseline measurements. A multiresolution framework for model data covariance computation and parallelization of the beamformer implementation enables efficient high-resolution reconstruction images. Results with both simulations and real datasets indicate that multiple highly correlated sources can be accurately reconstructed, and that interference and noise can be sufficiently suppressed. Reconstructions at 2-2.5mm resolution (  âˆ¼  150K voxels) are possible with efficient run times of 1-3 minutes. This novel adaptive beamforming algorithm significantly outperforms the state-of-the-art benchmarks. Therefore, SBL-BF provides an effective framework for efficiently reconstructing multiple correlated brain sources with high resolution and robustness to interference and noise.

Bayesian Algorithms for Joint Estimation of Brain Activity and Noise in Electromagnetic Imaging.

Simultaneously estimating brain source activity and noise has long been a challenging task in electromagnetic brain imaging using magneto- and electroencephalography. The problem is challenging not only in terms of solving the NP-hard inverse problem of reconstructing unknown brain activity across thousands of voxels from a limited number of sensors, but also for the need to simultaneously estimate the noise and interference. We present a generative model with an augmented leadfield matrix to simultaneously estimate brain source activity and sensor noise statistics in electromagnetic brain imaging (EBI). We then derive three Bayesian inference algorithms for this generative model (expectation-maximization (EBI-EM), convex bounding (EBI-Convex) and fixed-point (EBI-Mackay)) to simultaneously estimate the hyperparameters of the prior distribution for brain source activity and sensor noise. A comprehensive performance evaluation for these three algorithms is performed. Simulations consistently show that the performance of EBI-Convex and EBI-Mackay updates is superior to that of EBI-EM. In contrast to the EBI-EM algorithm, both EBI-Convex and EBI-Mackay updates are quite robust to initialization, and are computationally efficient with fast convergence in the presence of both Gaussian and real brain noise. We also demonstrate that EBI-Convex and EBI-Mackay update algorithms can reconstruct complex brain activity with only a few trials of sensor data, and for resting-state data, achieving significant improvement in source reconstruction and noise learning for electromagnetic brain imaging.

Empirical Bayesian localization of event-related time-frequency neural activity dynamics.

Accurate reconstruction of the spatio-temporal dynamics of event-related cortical oscillations across human brain regions is an important problem in functional brain imaging and human cognitive neuroscience with magnetoencephalography (MEG) and electroencephalography (EEG). The problem is challenging not only in terms of localization of complex source configurations from sensor measurements with unknown noise and interference but also for reconstruction of transient event-related time-frequency dynamics of cortical oscillations. We recently proposed a robust empirical Bayesian algorithm for simultaneous reconstruction of complex brain source activity and noise covariance, in the context of evoked and resting-state data. In this paper, we expand upon this empirical Bayesian framework for optimal reconstruction of event-related time-frequency dynamics of regional cortical oscillations, referred to as time-frequency Champagne (TFC). This framework enables imaging of five-dimensional (space, time, and frequency) event-related brain activity from M/EEG data, and can be viewed as a time-frequency optimized adaptive Bayesian beamformer. We evaluate TFC in both simulations and several real datasets, with comparisons to benchmark standards - variants of time-frequency optimized adaptive beamformers (TFBF) as well as the sLORETA algorithm. In simulations, we demonstrate several advantages in estimating time-frequency cortical oscillatory dynamics compared to benchmarks. With real MEG data, we demonstrate across many datasets that the proposed approach is robust to highly correlated brain activity and low SNR data, and is able to accurately reconstruct cortical dynamics with data from just a few epochs.

Magnetoneurography as a novel functional imaging technique for the ulnar nerve at the elbow.

OBJECTIVE: To visualize the neural activity of the ulnar nerve at the elbow using magnetoneurography (MNG). METHODS: Subjects were asymptomatic volunteers (eight men and one woman; age, 26-53 years) and a male patient with cubital tunnel syndrome (age, 54 years). The ulnar nerve was electrically stimulated at the left wrist and evoked magnetic fields were recorded by a 132-channel biomagnetometer system with a superconducting quantum interference device at the elbow. Evoked potentials were also recorded and their correspondence to the evoked magnetic fields was evaluated in healthy participants. RESULTS: Evoked magnetic fields were successfully recorded by MNG, and computationally reconstructed currents were able to visualize the neural activity of the ulnar nerve at the elbow. In the affected arm of the patient, reconstructed intra-axonal and inflow currents attenuated and decelerated around the elbow. Latencies of reconstructed currents and evoked potentials were correspondent within an error of 0.4 ms in asymptomatic participants. CONCLUSIONS: Neural activity in the ulnar nerve can be visualized by MNG, which may be a novel functional imaging technique for ulnar neuropathy at the elbow, including cubital tunnel syndrome. SIGNIFICANCE: MNG permits visualization of evoked currents in the ulnar nerve at the cubital tunnel.

Assessing carpal tunnel syndrome with magnetoneurography.

OBJECTIVE: To measure the neuromagnetic fields of carpal tunnel syndrome patients after electrical digital nerve stimulation and evaluate median nerve function with high spatial resolution. METHODS: A superconducting quantum interference device magnetometer system was used to record neuromagnetic fields at the carpal tunnel after electrical stimulation of the middle digital nerve in 10 hands of nine patients with carpal tunnel syndrome. The patients were diagnosed based on symptoms (numbness, tingling, and pain) supported by a positive Phalen or Tinel sign. A novel technique was applied to remove stimulus-induced artifacts, and current distributions were calculated using a spatial filter algorithm and superimposed on X-ray. RESULTS: In 6 of the 10 hands, the amplitude of the inward current waveform attenuated to <70% or the nerve conduction velocity was <40 m/s. The results of conventional nerve conduction studies were normal for two of these six hands. All four hands that could not be diagnosed by magnetoneurography had severe carpal tunnel syndrome superimposed on peripheral neuropathy secondary to comorbidities. CONCLUSIONS: Technical improvements enabled magnetoneurography to noninvasively visualize the electrophysiological nerve activity in carpal tunnel syndrome patients. SIGNIFICANCE: Magnetoneurography may have the potential to contribute to the detailed diagnosis of various peripheral nerve disorders.

Assessment of thoracic spinal cord electrophysiological activity through magnetoneurography.

OBJECTIVE: Noninvasive and detailed visualization of electrophysiological activity in the thoracic spinal cord through magnetoneurography. METHODS: In five healthy volunteers, magnetic fields around current flowing in the thoracic spinal cord after alternating unilateral and synchronized bilateral sciatic nerve stimulation were measured using a magnetoneurograph system with superconductive quantum interference device biomagnetometers. The current distribution was obtained from the magnetic data by spatial filtering and visualized by superimposing it on the X-ray image. Conduction velocity was calculated using the peak latency of the current waveforms. RESULTS: A sufficiently high magnetic signal intensity and signal-to-noise ratio were obtained in all participants after synchronized bilateral sciatic nerve stimulation. Leading and trailing components along the spinal canal and inward components flowing into the depolarization site ascended to the upper thoracic spine. Conduction velocity of the inward current in the whole thoracic spine was 42.4 m/s. CONCLUSIONS: Visualization of electrophysiological activity in the thoracic spinal cord was achieved through magnetoneurography and a new method for synchronized bilateral sciatic nerve stimulation. Magnetoneurography is expected to be a useful modality in functional assessment of thoracic myelopathy. SIGNIFICANCE: This is the first report to use magnetoneurography to noninvasively visualize electrophysiological activity in the thoracic spinal cord in detail.

Clinical Validation of the Champagne Algorithm for Epilepsy Spike Localization.

Magnetoencephalography (MEG) is increasingly used for presurgical planning in people with medically refractory focal epilepsy. Localization of interictal epileptiform activity, a surrogate for the seizure onset zone whose removal may prevent seizures, is challenging and depends on the use of multiple complementary techniques. Accurate and reliable localization of epileptiform activity from spontaneous MEG data has been an elusive goal. One approach toward this goal is to use a novel Bayesian inference algorithm-the Champagne algorithm with noise learning-which has shown tremendous success in source reconstruction, especially for focal brain sources. In this study, we localized sources of manually identified MEG spikes using the Champagne algorithm in a cohort of 16 patients with medically refractory epilepsy collected in two consecutive series. To evaluate the reliability of this approach, we compared the performance to equivalent current dipole (ECD) modeling, a conventional source localization technique that is commonly used in clinical practice. Results suggest that Champagne may be a robust, automated, alternative to manual parametric dipole fitting methods for localization of interictal MEG spikes, in addition to its previously described clinical and research applications.

Noninvasive measurement of sensory action currents in the cervical cord by magnetospinography.

OBJECTIVE: To obtain magnetic recordings of electrical activities in the cervical cord and visualize sensory action currents of the dorsal column, intervertebral foramen, and dorsal horn. METHODS: Neuromagnetic fields were measured at the neck surface upon median nerve stimulation at the wrist using a magnetospinography system with high-sensitivity superconducting quantum interference device sensors. Somatosensory evoked potentials (SEPs) were also recorded. Evoked electrical currents were reconstructed by recursive null-steering beamformer and superimposed on cervical X-ray images. RESULTS: Estimated electrical currents perpendicular to the cervical cord ascended sequentially. Their peak latency at C5 and N11 peak latency of SEP were well-correlated in all 16 participants (r = 0.94, p < 0.0001). Trailing axonal currents in the intervertebral foramens were estimated in 10 participants. Estimated dorsal-ventral electrical currents were obtained within the spinal canal at C5. Current density peak latency significantly correlated with cervical N13-P13 peak latency of SEPs in 13 participants (r = 0.97, p < 0.0001). CONCLUSIONS: Magnetospinography shows excellent spatial and temporal resolution after median nerve stimulation and can identify the spinal root entry level, calculate the dorsal column conduction velocity, and analyze segmental dorsal horn activity. SIGNIFICANCE: This approach is useful for functional electrophysiological diagnosis of somatosensory pathways.

Robust estimation of noise for electromagnetic brain imaging with the champagne algorithm.

Robust estimation of the number, location, and activity of multiple correlated brain sources has long been a challenging task in electromagnetic brain imaging from M/EEG data, one that is significantly impacted by interference from spontaneous brain activity, sensor noise, and other sources of artifacts. Recently, we introduced the Champagne algorithm, a novel Bayesian inference algorithm that has shown tremendous success in M/EEG source reconstruction. Inherent to Champagne and most other related Bayesian reconstruction algorithms is the assumption that the noise covariance in sensor data can be estimated from "baseline" or "control" measurements. However, in many scenarios, such baseline data is not available, or is unreliable, and it is unclear how best to estimate the noise covariance. In this technical note, we propose several robust methods to estimate the contributions to sensors from noise arising from outside the brain without the need for additional baseline measurements. The incorporation of these methods for diagonal noise covariance estimation improves the robust reconstruction of complex brain source activity under high levels of noise and interference, while maintaining the performance features of Champagne. Specifically, we show that the resulting algorithm, Champagne with noise learning, is quite robust to initialization and is computationally efficient. In simulations, performance of the proposed noise learning algorithm is consistently superior to Champagne without noise learning. We also demonstrate that, even without the use of any baseline data, Champagne with noise learning is able to reconstruct complex brain activity with just a few trials or even a single trial, demonstrating significant improvements in source reconstruction for electromagnetic brain imaging.

NUTMEG: Open Source Software for M/EEG Source Reconstruction.

Neurodynamic Utility Toolbox for Magnetoencephalo- and Electroencephalography (NUTMEG) is an open-source MATLAB-based toolbox for the analysis and reconstruction of magnetoencephalography/electroencephalography data in source space. NUTMEG includes a variety of options for the user in data import, preprocessing, source reconstruction, and functional connectivity. A group analysis toolbox allows the user to run a variety of inferential statistics on their data in an easy-to-use GUI-driven format. Importantly, NUTMEG features an interactive five-dimensional data visualization platform. A key feature of NUTMEG is the availability of a large menu of interference cancelation and source reconstruction algorithms. Each NUTMEG operation acts as a stand-alone MATLAB function, allowing the package to be easily adaptable and scripted for the more advanced user for interoperability with other software toolboxes. Therefore, NUTMEG enables a wide range of users access to a complete "sensor-to- source-statistics" analysis pipeline.

Visualization of electrical activity in the cervical spinal cord and nerve roots after ulnar nerve stimulation using magnetospinography.

OBJECTIVE: To establish a method for magnetospinography (MSG) measurement after ulnar nerve stimulation and to clarify its characteristics. METHODS: Using a 132-channel magnetoneurography system with a superconducting quantum interference device, cervical MSG measurements were obtained for 10 healthy volunteers after stimulation of the ulnar nerve at the elbow and the wrist, and neural current distribution was calculated and superimposed on the cervical X-ray images. RESULTS: Neuromagnetic signals were obtained in all participants after applying the stimulus artifact removal algorithm. The measured magnetic field intensity after elbow stimulation was about twice that after wrist stimulation. Calculated neural currents flowed into the intervertebral foramina at C6/7 to T1/2 and propagated cranially along the spinal canal. The conduction velocity from the peak latency of inward currents at C5-C7 was 73.4 ± 19.6 m/s. CONCLUSIONS: We successfully obtained MSG measurements after ulnar nerve stimulation. The neural currents flowed into the spinal canal from more caudal segments after ulnar nerve stimulation compared with median nerve stimulation, and these MSG measurements were effective in examining the spinal tracts at C5/6/7. SIGNIFICANCE: This is the first report on the use of MSG to visualize electrical activity in the cervical spinal cord and nerve root after ulnar nerve stimulation.

Evaluation of neural activity by magnetospinography with 3D sensors.

OBJECTIVE: Magnetospinography (MSG) has been developed for clinical application and is expected to be a novel neurophysiological examination. Here, we used an MSG system with sensors positioned in three orthogonal directions to record lumbar canal evoked magnetic fields (LCEFs) in response to peripheral nerve stimulation and to evaluate methods for localizing spinal cord lesions. METHODS: LCEFs from the lumbar area of seven rabbits were recorded by the MSG system in response to electrical stimulation of a sciatic nerve. LCEFs and lumbar canal evoked potentials (LCEPs) were measured before and after spinal cord compression induced by a balloon catheter. The lesion positions were estimated using LCEPs and computationally reconstructed currents corresponding to the depolarization site. RESULTS: LCEFs were recorded in all rabbits and neural activity in the lumbar spinal cord could be visualized in the form of a magnetic contour map and reconstructed current map. The position of the spinal cord lesion could be estimated by the LCEPs and reconstructed currents at the depolarization site. CONCLUSIONS: MSG can visualize neural activity in the spinal cord and localize the lesion site. SIGNIFICANCE: MSG enables noninvasive assessment of neural activity in the spinal canal using currents at depolarization sites reconstructed from LCEFs.

Robust Empirical Bayesian Reconstruction of Distributed Sources for Electromagnetic Brain Imaging.

Electromagnetic brain imaging is the reconstruction of brain activity from non-invasive recordings of the magnetic fields and electric potentials. An enduring challenge in this imaging modality is estimating the number, location, and time course of sources, especially for the reconstruction of distributed brain sources with complex spatial extent. Here, we introduce a novel robust empirical Bayesian algorithm that enables better reconstruction of distributed brain source activity with two key ideas: kernel smoothing and hyperparameter tiling. Since the proposed algorithm builds upon many of the performance features of the sparse source reconstruction algorithm - Champagne and we refer to this algorithm as Smooth Champagne. Smooth Champagne is robust to the effects of high levels of noise, interference, and highly correlated brain source activity. Simulations demonstrate excellent performance of Smooth Champagne when compared to benchmark algorithms in accurately determining the spatial extent of distributed source activity. Smooth Champagne also accurately reconstructs real MEG and EEG data.

Visualization of electrophysiological activity at the carpal tunnel area using magnetoneurography.

OBJECTIVE: To establish a noninvasive method to measure the neuromagnetic fields of the median nerve at the carpal tunnel after electrical digital nerve stimulation and evaluate peripheral nerve function. METHODS: Using a vector-type biomagnetometer system with a superconducting quantum interference device, neuromagnetic fields at the carpal tunnel were recorded after electrical stimulation of the index or middle digital nerve in five healthy volunteers. A novel technique for removing stimulus-induced artifacts was applied, and current distributions were calculated using a spatial filter algorithm and superimposed on X-ray. RESULTS: A neuromagnetic field propagating from the palm to the carpal tunnel was observed in all participants. Current distributions estimated from the magnetic fields had five components: leading and trailing components parallel to the conduction pathway, outward current preceding the leading component, inward currents between the leading and trailing components, and outward current following the trailing component. The conduction velocity and peak latency of the inward current agreed well with those of sensory nerve action potentials. CONCLUSION: Removing stimulus-induced artifacts enabled magnetoneurography to noninvasively visualize with high spatial resolution the electrophysiological neural activity from the palm to the carpal tunnel. SIGNIFICANCE: This is the first report of using magnetoneurography to visualize electrophysiological nerve activity at the palm and carpal tunnel.

Noninvasive Mapping of Premature Ventricular Contractions by Merging Magnetocardiography and Computed Tomography.

OBJECTIVES: This study aimed to develop a novel premature ventricular contraction (PVC) mapping method to predict PVC origins in whole ventricles by merging a magnetocardiography (MCG) image with a cardiac computed tomography (CT) image. BACKGROUND: MCG can noninvasively discriminate PVCs originating from the aortic sinus cusp from those originating from the right ventricular outflow tract. METHODS: This study was composed of 22 candidates referred for catheter ablation of idiopathic PVCs. MCG and CT were performed the same day before ablation. Estimated origins by MCG-CT imaging using the recursive null steering spatial filter algorithm were compared with origins determined by electroanatomic mapping (CARTO, Biosense Webster, Inc., Diamond Bar, California) during the ablation procedure. Radiopaque acrylic markers for the CT scan and coil markers generating a weak magnetic field during MCG measurements were used as reference markers to merge the 2 images 3-dimensionally. RESULTS: PVC origins were determined by endocardial and epicardial mapping and ablation results in 18 (86%) patients (right ventricular outflow tract in 10 patients, aortic sinus cusp in 2 patients, interventricular septum in 1 patient, near His bundle in 1 patient, right ventricular free wall in 1 patient, and left ventricular free wall in 3 patients). Estimated origins by MCG-CT imaging matched the origins determined during the procedure in 94% (17 of 18) of patients, whereas the electrocardiography algorithms were accurate in only 56% (10 of 18). Discrimination of an epicardium versus an endocardium or right- versus left-sided septum was successful in 3 of 4 patients (75%). CONCLUSIONS: The diagnostic accuracy of noninvasive MCG-CT mapping was high enough to allow clinical use to predict the site of PVC origins in the whole ventricles.

Novel functional imaging technique for the brachial plexus based on magnetoneurography.

OBJECTIVE: To visualize neural activity in the brachial plexus using magnetoneurography (MNG). METHODS: Using a 124- or 132-channel biomagnetometer system with a superconducting quantum interference device, neuromagnetic fields above the clavicle and neck region were recorded in response to electrical stimulation of the median and ulnar nerves in five asymptomatic volunteers (four men and one woman; age, 27-45 years old). Equivalent currents were computationally reconstructed from neuromagnetic fields and visualized as pseudocolor maps. Reconstructed currents at the depolarization site and compound nerve action potentials (CNAPs) at Erb's point were compared. RESULTS: Neuromagnetic fields were recorded in all subjects. The reconstructed equivalent currents propagated into the vertebral foramina, and the main inflow levels differed between the median nerve (C5/C6-C7/T1 vertebral foramen) and the ulnar nerve (C7/T1-T1/T2). The inward current peaks at the depolarization site and CNAPs showed high linear correlation. CONCLUSIONS: MNG visualizes neural activity in the brachial plexus and can differentiate the conduction pathways after median and ulnar nerve stimulations. In addition, it can visualize not only the leading and trailing components of intra-axonal currents, but also inward currents at the depolarization site. SIGNIFICANCE: MNG is a novel and promising functional imaging modality for the brachial plexus.

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.