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.

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.

Evaluation of a dual signal subspace projection algorithm in magnetoencephalographic recordings from patients with intractable epilepsy and vagus nerve stimulators.

Magnetoencephalography (MEG) data is subject to many sources of environmental noise, and interference rejection is a necessary step in the processing of MEG data. Large amplitude interference caused by sources near the brain have been common in clinical settings and are difficult to reject. Artifact from vagal nerve stimulators (VNS) is a prototypical example. In this study, we describe a novel MEG interference rejection algorithm called dual signal subspace projection (DSSP), and evaluate its performance in clinical MEG data from people with epilepsy and implanted VNS. The performance of DSSP was evaluated in a retrospective cohort study of patients with epilepsy and VNS who had MEG scans for source localization of interictal epileptiform discharges. DSSP was applied to the MEG data and compared with benchmark for performance. We evaluated the clinical impact of interference rejection based on human expert detection and estimation of the location and time-course of interictal spikes, using an empirical Bayesian source reconstruction algorithm (Champagne). Clinical recordings, after DSSP processing, became more readable and a greater number of interictal epileptic spikes could be clearly identified. Source localization results of interictal spikes also significantly improved from those achieved before DSSP processing, including meaningful estimates of activity time courses. Therefore, DSSP is a valuable novel interference rejection algorithm that can be successfully deployed for the removal of strong artifacts and interferences in MEG.

Magnetocardiography Using a Magnetoresistive Sensor Array.

In previous magnetocardiography studies, magnetocardiograms (MCGs) have been obtained using superconducting quantum interference device (SQUID) systems. SQUID is the most sensitive instrument for measuring low-frequency magnetic fields, but it requires liquid helium for cooling, so operating costs are high. In contrast, magnetoresistive (MR) magnetometers function by detecting the change in resistance, caused by an external magnetic field, and have much lower costs. This study was aimed to evaluate feasibility of the MR sensor array for acquiring MCGs.We used an MR sensor array, which was developed for measuring magnetic fields in the picotesla range, with a reduced noise level (TDK Corporation, Tokyo, Japan). A 30-channel MR sensor array was placed in a magnetically shielded room, and the cardiac magnetic field over the anterior chest walls of five healthy subjects was recorded.For all five subjects, MCGs were successfully recorded using the MR sensor array. The cardiac magnetic field corresponding to P, QRS, and T waves on an electrocardiogram (ECG) was detectable by signals averaging 272 ± 27.5 beats.An MR sensor array can be used to measure cardiac magnetic fields. Our results will contribute to the development of low-cost devices for recording MCGs, which will help develop non-invasive diagnostics in cardiovascular medicine.

Hierarchical multiscale Bayesian algorithm for robust MEG/EEG source reconstruction.

In this paper, we present a novel hierarchical multiscale Bayesian algorithm for electromagnetic brain imaging using magnetoencephalography (MEG) and electroencephalography (EEG). In particular, we present a solution to the source reconstruction problem for sources that vary in spatial extent. We define sensor data measurements using a generative probabilistic graphical model that is hierarchical across spatial scales of brain regions and voxels. We then derive a novel Bayesian algorithm for probabilistic inference with this graphical model. This algorithm enables robust reconstruction of sources that have different spatial extent, from spatially contiguous clusters of dipoles to isolated dipolar sources. We compare the new algorithm with several representative benchmarks on both simulated and real brain activities. The source locations and the correct estimation of source time courses used for the simulated data are chosen to test the performance on challenging source configurations. In simulations, performance of the novel algorithm shows superiority to several existing benchmark algorithms. We also demonstrate that the new algorithm is more robust to correlated brain activity present in real MEG and EEG data and is able to resolve distinct and functionally relevant brain areas with real MEG and EEG datasets.

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 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.

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.

Performance evaluation of the Champagne source reconstruction algorithm on simulated and real M/EEG data.

In this paper, we present an extensive performance evaluation of a novel source localization algorithm, Champagne. It is derived in an empirical Bayesian framework that yields sparse solutions to the inverse problem. It is robust to correlated sources and learns the statistics of non-stimulus-evoked activity to suppress the effect of noise and interfering brain activity. We tested Champagne on both simulated and real M/EEG data. The source locations used for the simulated data were chosen to test the performance on challenging source configurations. In simulations, we found that Champagne outperforms the benchmark algorithms in terms of both the accuracy of the source localizations and the correct estimation of source time courses. We also demonstrate that Champagne is more robust to correlated brain activity present in real MEG data and is able to resolve many distinct and functionally relevant brain areas with real MEG and EEG data.

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.

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.

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.

Robust Bayesian estimation of the location, orientation, and time course of multiple correlated neural sources using MEG.

The synchronous brain activity measured via MEG (or EEG) can be interpreted as arising from a collection (possibly large) of current dipoles or sources located throughout the cortex. Estimating the number, location, and time course of these sources remains a challenging task, one that is significantly compounded by the effects of source correlations and unknown orientations and by the presence of interference from spontaneous brain activity, sensor noise, and other artifacts. This paper derives an empirical Bayesian method for addressing each of these issues in a principled fashion. The resulting algorithm guarantees descent of a cost function uniquely designed to handle unknown orientations and arbitrary correlations. Robust interference suppression is also easily incorporated. In a restricted setting, the proposed method is shown to produce theoretically zero reconstruction error estimating multiple dipoles even in the presence of strong correlations and unknown orientations, unlike a variety of existing Bayesian localization methods or common signal processing techniques such as beamforming and sLORETA. Empirical results on both simulated and real data sets verify the efficacy of this approach.

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.

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.

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.