Cross-Axis Dynamic Field Compensation of Optically Pumped Magnetometer Arrays for MEG.

We present dynamic field compensation (DFC), whereby three-axis field measurements from reference magnetometers are used to dynamically maintain null at the alkali vapor cells of an array of primary sensors that are proximal to a subject's scalp. Precision measurement of the magnetoencephalogram (MEG) by zero-field optically pumped magnetometer (OPM) sensors requires that sensor response is linear and sensor gain is constant over time. OPMs can be operated in open-loop mode, where the measured field is proportional to the output at the demodulated photodiode output, or in closed-loop, where on-board coils are dynamically driven to maintain the internal cell at zero field in the measurement direction. While OPMs can be operated in closed-loop mode along all three axes, this can increase sensor noise and poses engineering challenges. Uncompensated fluctuations in the ambient field along any statically nulled axes perturb the measured field by tipping the measurement axis and altering effective sensor gain - a phenomenon recently referred to as cross-axis projection error (CAPE). These errors are particularly problematic when OPMs are allowed to move in the remnant background field. Sensor gain-errors, if not mitigated, preclude precision measurements with OPMs operating in the presence of ambient field fluctuations within a typical MEG laboratory. In this manuscript, we present the cross-axis dynamic field compensation (DFC) method for maintaining zero field dynamically on all three axes of each sensor in an array of OPMs. Together, DFC and closed-loop operation strongly attenuate errors introduced by CAPE. This method was implemented by using three orthogonal reference sensors together with OPM electronics that permit driving each sensor's transverse field coils dynamically to maintain null field across its OPM measurement cell. These reference sensors can also be used for synthesizing 1st-gradient response to further reduce the effects of fluctuating ambient fields on measured brain activity and compensate for movement within a uniform field. We demonstrate that, using the DFC method, magnetic field measurement errors of less than 0.7% are easily achieved for an array of OPM sensors in the presence of ambient field perturbations of several nT.

On-scalp magnetocorticography with optically pumped magnetometers: Simulated performance in resolving simultaneous sources.

Currently, the gold standard for high-resolution mapping of cortical electrophysiological activity is invasive electrocorticography (ECoG), a procedure that carries with it the risk of serious morbidity and mortality. Due to these risks, the use of ECoG is largely limited to pre-surgical mapping in intractable epilepsy. Nevertheless, many seminal studies in neuroscience have utilized ECoG to explore domains such as visual perception, attention, auditory processing, and sensorimotor behavior. Studies such as these, occurring in patients with epilepsy rather than healthy controls, may lack generalizability, and are limited by the placement of the electrode arrays over the presumed seizure focus. This manuscript explores the use of optically pumped magnetometers (OPMs) to create a non-invasive alternative to ECoG, which we refer to as magnetocorticography. Because prior ECoG studies reveal that most cognitive processes are driven by multiple, simultaneous independent neuronal assemblies, we characterize the ability of a theoretical 56-channel dense OPM array to resolve simultaneous independent sources, and compare it to currently available SQUID devices, as well as OPM arrays with inter-sensor spacings more typical of other systems in development. Our evaluation of this theoretical system assesses many potential sources of error, including errors of sensor calibration and position. In addition, we investigate the influence of geometrical and anatomical factors on array performance. Our simulations reveal the potential of high-density, on-scalp OPM MEG devices to localize electrophysiological brain responses at unprecedented resolution for a non-invasive device.

A set of electroencephalographic (EEG) data recorded during amplitude-modulated transcranial alternating current stimulation (AM-tACS) targeting 10-Hz steady-state visually evoked potentials (SSVEP).

Transcranial alternating current stimulation (tACS) can affect perception, learning and cognition, but the underlying mechanisms are not well understood. A promising strategy to elucidate these mechanisms aims at applying tACS while electric or magnetic brain oscillations targeted by stimulation are recorded. However, reconstructing brain oscillations targeted by tACS remains a challenging problem due to stimulation artifacts. Besides lack of an established strategy to effectively supress such stimulation artifacts, there are also no resources available that allow for the development and testing of new and effective tACS artefact suppression algorithms, such as adaptive spatial filtering using beamforming or signal-space projection. Here, we provide a full dataset comprising encephalographic (EEG) recordings across six healthy human volunteers who underwent 10-Hz amplitude-modulated tACS (AM-tACS) during a 10-Hz steady-state visually evoked potential (SSVEP) paradigm. Moreover, data and scripts are provided related to the validation of a novel stimulation artefact suppression strategy, Stimulation Artifact Source Separation (SASS), removing EEG signal components that are maximally different in the presence versus absence of stimulation. Besides including EEG single-trial data and comparisons of 10-Hz brain oscillatory phase and amplitude recorded across three conditions (condition 1: no stimulation, condition 2: stimulation with SASS, condition 3: stimulation without SASS), also power spectra and topographies of SSVEP amplitudes across all three conditions are presented. Moreover, data is provided for assessing nonlinear modulations of the stimulation artifact in both time and frequency domains due to heartbeats. Finally, the dataset includes eigenvalue spectra and spatial patterns of signal components that were identified and removed by SASS for stimulation artefact suppression at the target frequency. Besides providing a valuable resource to assess properties of AM-tACS artifacts in the EEG, this dataset allows for testing different artifact rejection methods and offers in-depth insights into the workings of SASS.

Stimulation artifact source separation (SASS) for assessing electric brain oscillations during transcranial alternating current stimulation (tACS).

Brain oscillations, e.g. measured by electro- or magnetoencephalography (EEG/MEG), are causally linked to brain functions that are fundamental for perception, cognition and learning. Recent advances in neurotechnology provide means to non-invasively target these oscillations using frequency-tuned amplitude-modulated transcranial alternating current stimulation (AM-tACS). However, online adaptation of stimulation parameters to ongoing brain oscillations remains an unsolved problem due to stimulation artifacts that impede such adaptation, particularly at the target frequency. Here, we introduce a real-time compatible artifact rejection algorithm (Stimulation Artifact Source Separation, SASS) that overcomes this limitation. SASS is a spatial filter (linear projection) removing EEG signal components that are maximally different in the presence versus absence of stimulation. This enables the reliable removal of stimulation-specific signal components, while leaving physiological signal components unaffected. For validation of SASS, we evoked brain activity with known phase and amplitude using 10 Hz visual flickers across 7 healthy human volunteers. 64-channel EEG was recorded during and in absence of 10 Hz AM-tACS targeting the visual cortex. Phase differences between AM-tACS and the visual stimuli were randomized, so that steady-state visually evoked potentials (SSVEPs) were phase-locked to the visual stimuli but not to the AM-tACS signal. For validation, distributions of single-trial amplitude and phase of EEG signals recorded during and in absence of AM-tACS were compared for each participant. When no artifact rejection method was applied, AM-tACS stimulation artifacts impeded assessment of single-trial SSVEP amplitude and phase. Using SASS, amplitude and phase of single trials recorded during and in absence of AM-tACS were comparable. These results indicate that SASS can be used to establish adaptive (closed-loop) AM-tACS, a potentially powerful tool to target various brain functions, and to investigate how AM-tACS interacts with electric brain oscillations.

Deriving frequency-dependent spatial patterns in MEG-derived resting state sensorimotor network: A novel multiband ICA technique.

Recently, independent components analysis (ICA) of resting state magnetoencephalography (MEG) recordings has revealed resting state networks (RSNs) that exhibit fluctuations of band-limited power envelopes. Most of the work in this area has concentrated on networks derived from the power envelope of beta bandpass-filtered data. Although research has demonstrated that most networks show maximal correlation in the beta band, little is known about how spatial patterns of correlations may differ across frequencies. This study analyzed MEG data from 18 healthy subjects to determine if the spatial patterns of RSNs differed between delta, theta, alpha, beta, gamma, and high gamma frequency bands. To validate our method, we focused on the sensorimotor network, which is well-characterized and robust in both MEG and functional magnetic resonance imaging (fMRI) resting state data. Synthetic aperture magnetometry (SAM) was used to project signals into anatomical source space separately in each band before a group temporal ICA was performed over all subjects and bands. This method preserved the inherent correlation structure of the data and reflected connectivity derived from single-band ICA, but also allowed identification of spatial spectral modes that are consistent across subjects. The implications of these results on our understanding of sensorimotor function are discussed, as are the potential applications of this technique. Hum Brain Mapp 38:779-791, 2017. © 2016 Wiley Periodicals, Inc.

Localization of Interictal Epileptic Spikes With MEG: Optimization of an Automated Beamformer Screening Method (SAMepi) in a Diverse Epilepsy Population.

PURPOSE: To describe and optimize an automated beamforming technique followed by identification of locations with excess kurtosis (g2) for efficient detection and localization of interictal spikes in patients with medically refractory epilepsy. METHODS: Synthetic aperture magnetometry with g2 averaged over a sliding time window (SAMepi) was performed in seven patients with focal epilepsy and five healthy volunteers. The effect of varied window lengths on detection of spiking activity was evaluated. RESULTS: Sliding window lengths of 0.5 to 10 seconds performed similarly, with 0.5- and 1-second windows detecting spiking activity in 1 of the 3 virtual sensor locations with highest kurtosis. These locations were concordant with the region of eventual surgical resection in these seven patients who remained seizure-free at 1 year. Average g2 values increased with increasing sliding window length in all subjects. In healthy volunteers, kurtosis values stabilized in data sets longer than 2 minutes. CONCLUSIONS: SAMepi using g2 averaged over 1-second sliding time windows in data sets of at least 2 minutes of duration reliably identified interictal spiking and the presumed seizure focus in these seven patients. Screening the five locations with highest kurtosis values for spiking activity is an efficient and accurate technique for localizing interictal activity using magnetoencephalography. SIGNIFICANCE: SAMepi should be applied using the parameter values and procedure described for optimal detection and localization of interictal spikes. Use of this screening procedure could significantly improve the efficiency of magnetoencephalography analysis if clinically validated.

Preliminary differences in resting state MEG functional connectivity pre- and post-ketamine in major depressive disorder.

Functional neuroimaging techniques including magnetoencephalography (MEG) have demonstrated that the brain is organized into networks displaying correlated activity. Group connectivity differences between healthy controls and participants with major depressive disorder (MDD) can be detected using temporal independent components analysis (ICA) on beta-bandpass filtered Hilbert envelope MEG data. However, the response of these networks to treatment is unknown. Ketamine, an N-methyl-D-aspartate (NMDA) receptor antagonist, exerts rapid antidepressant effects. We obtained MEG recordings before and after open-label infusion of 0.5mg/kg ketamine in MDD subjects (N=13) and examined networks previously shown to differ between healthy individuals and those with MDD. Connectivity between the amygdala and an insulo-temporal component decreased post-ketamine in MDD subjects towards that observed in control subjects at baseline. Decreased baseline connectivity of the subgenual anterior cingulate cortex (sgACC) with a bilateral precentral network had previously been observed in MDD compared to healthy controls, and the change in connectivity post-ketamine was proportional to the change in sgACC glucose metabolism in a subset (N=8) of subjects receiving [11F]FDG-PET imaging. Ketamine appeared to reduce connectivity, regardless of whether connectivity was abnormally high or low compared to controls at baseline. These preliminary findings suggest that sgACC connectivity may be directly related to glutamate levels.

tACS Phase Locking of Frontal Midline Theta Oscillations Disrupts Working Memory Performance.

BACKGROUND: Frontal midline theta (FMT) oscillations (4-8 Hz) are strongly related to cognitive and executive control during mental tasks such as memory processing, arithmetic problem solving or sustained attention. While maintenance of temporal order information during a working memory (WM) task was recently linked to FMT phase, a positive correlation between FMT power, WM demand and WM performance was shown. However, the relationship between these measures is not well understood, and it is unknown whether purposeful FMT phase manipulation during a WM task impacts FMT power and WM performance. Here we present evidence that FMT phase manipulation mediated by transcranial alternating current stimulation (tACS) can block WM demand-related FMT power increase (FMTΔpower) and disrupt normal WM performance. METHODS: Twenty healthy volunteers were assigned to one of two groups (group A, group B) and performed a 2-back task across a baseline block (block 1) and an intervention block (block 2) while 275-sensor magnetoencephalography (MEG) was recorded. After no stimulation was applied during block 1, participants in group A received tACS oscillating at their individual FMT frequency over the prefrontal cortex (PFC) while group B received sham stimulation during block 2. After assessing and mapping phase locking values (PLV) between the tACS signal and brain oscillatory activity across the whole brain, FMT power and WM performance were assessed and compared between blocks and groups. RESULTS: During block 2 of group A but not B, FMT oscillations showed increased PLV across task-related cortical areas underneath the frontal tACS electrode. While WM task-related FMTΔpower and WM performance were comparable across groups in block 1, tACS resulted in lower FMTΔpower and WM performance compared to sham stimulation in block 2. CONCLUSION: tACS-related manipulation of FMT phase can disrupt WM performance and influence WM task-related FMTΔpower. This finding may have important implications for the treatment of brain disorders such as depression and attention deficit disorder associated with abnormal regulation of FMT activity or disorders characterized by dysfunctional coupling of brain activity, e.g., epilepsy, Alzheimer's or Parkinson's disease (AD/PD).

Mapping entrained brain oscillations during transcranial alternating current stimulation (tACS).

Transcranial alternating current stimulation (tACS), a non-invasive and well-tolerated form of electric brain stimulation, can influence perception, memory, as well as motor and cognitive function. While the exact underlying neurophysiological mechanisms are unknown, the effects of tACS are mainly attributed to frequency-specific entrainment of endogenous brain oscillations in brain areas close to the stimulation electrodes, and modulation of spike timing dependent plasticity reflected in gamma band oscillatory responses. tACS-related electromagnetic stimulator artifacts, however, impede investigation of these neurophysiological mechanisms. Here we introduce a novel approach combining amplitude-modulated tACS during whole-head magnetoencephalography (MEG) allowing for artifact-free source reconstruction and precise mapping of entrained brain oscillations underneath the stimulator electrodes. Using this approach, we show that reliable reconstruction of neuromagnetic low- and high-frequency oscillations including high gamma band activity in stimulated cortical areas is feasible opening a new window to unveil the mechanisms underlying the effects of stimulation protocols that entrain brain oscillatory activity.

Simultaneous transcranial direct current stimulation (tDCS) and whole-head magnetoencephalography (MEG): assessing the impact of tDCS on slow cortical magnetic fields.

Transcranial direct current stimulation (tDCS) can influence cognitive, affective or motor brain functions. Whereas previous imaging studies demonstrated widespread tDCS effects on brain metabolism, direct impact of tDCS on electric or magnetic source activity in task-related brain areas could not be confirmed due to the difficulty to record such activity simultaneously during tDCS. The aim of this proof-of-principal study was to demonstrate the feasibility of whole-head source localization and reconstruction of neuromagnetic brain activity during tDCS and to confirm the direct effect of tDCS on ongoing neuromagnetic activity in task-related brain areas. Here we show for the first time that tDCS has an immediate impact on slow cortical magnetic fields (SCF, 0-4Hz) of task-related areas that are identical with brain regions previously described in metabolic neuroimaging studies. 14 healthy volunteers performed a choice reaction time (RT) task while whole-head magnetoencephalography (MEG) was recorded. Task-related source-activity of SCFs was calculated using synthetic aperture magnetometry (SAM) in absence of stimulation and while anodal, cathodal or sham tDCS was delivered over the right primary motor cortex (M1). Source reconstruction revealed task-related SCF modulations in brain regions that precisely matched prior metabolic neuroimaging studies. Anodal and cathodal tDCS had a polarity-dependent impact on RT and SCF in primary sensorimotor and medial centro-parietal cortices. Combining tDCS and whole-head MEG is a powerful approach to investigate the direct effects of transcranial electric currents on ongoing neuromagnetic source activity, brain function and behavior.

Group differences in MEG-ICA derived resting state networks: Application to major depressive disorder.

UNLABELLED: Functional magnetic resonance imaging (fMRI) studies have revealed the existence of robust, interconnected brain networks exhibiting correlated low frequency fluctuations during rest, which can be derived by examining inherent spatio-temporal patterns in functional scans independent of any a priori model. In order to explore the electrophysiological underpinnings of these networks, analogous techniques have recently been applied to magnetoencephalography (MEG) data, revealing similar networks that exhibit correlated low frequency fluctuations in the power envelope of beta band (14-30Hz) power. However, studies to date using this technique have concentrated on healthy subjects, and no method has yet been presented for group comparisons. We extended the ICA resting state MEG method to enable group comparisons, and demonstrate the technique in a sample of subjects with major depressive disorder (MDD). We found that the intrinsic resting state networks evident in fMRI appeared to be disrupted in individuals with MDD compared to healthy participants, particularly in the subgenual cingulate, although the electrophysiological correlates of this are unknown. Networks extracted from a combined group of healthy and MDD participants were examined for differences between groups. Individuals with MDD showed reduced correlations between the subgenual anterior cingulate (sgACC) and hippocampus in a network with primary nodes in the precentral and middle frontal gyri. Individuals with MDD also showed increased correlations between insulo-temporal nodes and amygdala compared to healthy controls. To further support our methods and findings, we present test/re-test reliability on independent recordings acquired within the same session. Our results demonstrate that group analyses are possible with the resting state MEG-independent component analysis (ICA) technique, highlighting a new pathway for analysis and discovery. This study also provides the first evidence of altered sgACC connectivity with a motor network. This finding, reliable across multiple sessions, suggests that the sgACC may partially mediate the psychomotor symptoms of MDD via synchronized changes in beta-band power, and expands the idea of the sgACC as a hub region mediating cognitive and emotional symptomatic domains in MDD. Findings of increased connectivity between the amygdala and cortical nodes further support the role of amygdalar networks in mediated depressive symptomatology. CLINICAL TRIALS IDENTIFIER: NCT00024635 (ZIA-MH002927-04).

Complexity measures in magnetoencephalography: measuring "disorder" in schizophrenia.

This paper details a methodology which, when applied to magnetoencephalography (MEG) data, is capable of measuring the spatio-temporal dynamics of 'disorder' in the human brain. Our method, which is based upon signal entropy, shows that spatially separate brain regions (or networks) generate temporally independent entropy time-courses. These time-courses are modulated by cognitive tasks, with an increase in local neural processing characterised by localised and transient increases in entropy in the neural signal. We explore the relationship between entropy and the more established time-frequency decomposition methods, which elucidate the temporal evolution of neural oscillations. We observe a direct but complex relationship between entropy and oscillatory amplitude, which suggests that these metrics are complementary. Finally, we provide a demonstration of the clinical utility of our method, using it to shed light on aberrant neurophysiological processing in schizophrenia. We demonstrate significantly increased task induced entropy change in patients (compared to controls) in multiple brain regions, including a cingulo-insula network, bilateral insula cortices and a right fronto-parietal network. These findings demonstrate potential clinical utility for our method and support a recent hypothesis that schizophrenia can be characterised by abnormalities in the salience network (a well characterised distributed network comprising bilateral insula and cingulate cortices).

Improving the excess kurtosis (g2) method for localizing epileptic sources in magnetoencephalographic recordings.

OBJECTIVE: To suggest ways to apply the excess kurtosis estimator g2, in the detection of epileptic activity with magnetoencephalography, while avoiding its bias towards detecting high-amplitude, infrequent events. METHODS: Synthetic aperture magnetometry (SAM), combined with g2, was applied using window lengths ranging from 0.125 s to 32 s and with sum and maximum metrics on simulated data and recordings of two focal epilepsy patients. RESULTS: Comparing sources with different spike rates (two per second and one per 2s), the sum metric was most efficient when using a window of 0.25s. Simulations showed that the sum metric is insensitive to spike frequency when the window includes more than one spike. SAM(g2) images from long segments with maximum metric resulted in misleading images, showing the strongest activity away from the lesions. CONCLUSIONS: Using a sliding window and the sum metric is beneficial when imaging interictal spikes and status epilepticus. Windows should be short enough not to include more than one interictal event. For continuous events such as electrographic seizures windows should contain baseline data and the epileptic event. SIGNIFICANCE: The sliding window and metric should be set according to the suggested guidelines when using SAM(g2) for presurgical evaluation.

Convergent BOLD and Beta-Band Activity in Superior Temporal Sulcus and Frontolimbic Circuitry Underpins Human Emotion Cognition.

The processing of social information in the human brain is widely distributed neuroanatomically and finely orchestrated over time. However, a detailed account of the spatiotemporal organization of these key neural underpinnings of human social cognition remains to be elucidated. Here, we applied functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) in the same participants to investigate spatial and temporal neural patterns evoked by viewing videos of facial muscle configurations. We show that observing the emergence of expressions elicits sustained blood oxygenation level-dependent responses in the superior temporal sulcus (STS), a region implicated in processing meaningful biological motion. We also found corresponding event-related changes in sustained MEG beta-band (14-30 Hz) oscillatory activity in the STS, consistent with the possible role of beta-band activity in visual perception. Dynamically evolving fearful and happy expressions elicited early (0-400 ms) transient beta-band activity in sensorimotor cortex that persisted beyond 400 ms, at which time it became accompanied by a frontolimbic spread (400-1000 ms). In addition, individual differences in sustained STS beta-band activity correlated with speed of emotion recognition, substantiating the behavioral relevance of these signals. This STS beta-band activity showed valence-specific coupling with the time courses of facial movements as they emerged into full-blown fearful and happy expressions (negative and positive coupling, respectively). These data offer new insights into the perceptual relevance and orchestrated function of the STS and interconnected pathways in social-emotion cognition.

In vivo assessment of human brain oscillations during application of transcranial electric currents.

Brain oscillations reflect pattern formation of cell assemblies' activity, which is often disturbed in neurological and psychiatric diseases like depression, schizophrenia and stroke. In the neurobiological analysis and treatment of these conditions, transcranial electric currents applied to the brain proved beneficial. However, the direct effects of these currents on brain oscillations have remained an enigma because of the inability to record them simultaneously. Here we report a novel strategy that resolves this problem. We describe accurate reconstructed localization of dipolar sources and changes of brain oscillatory activity associated with motor actions in primary cortical brain regions undergoing transcranial electric stimulation. This new method allows for the first time direct measurement of the effects of non-invasive electrical brain stimulation on brain oscillatory activity and behavior.

Spatiotemporal imaging of complexity.

What are the functional neuroimaging measurements required for more fully characterizing the events and locations of neocortical activity? A prime assumption has been that modulation of cortical activity will inevitably be reflected in changes in energy utilization (for the most part) changes of glucose and oxygen consumption. Are such a measures complete and sufficient? More direct measures of cortical electrophysiological activity show event or task-related modulation of amplitude or band-limited oscillatory power. Using magnetoencephalography (MEG), these measures have been shown to correlate well with energy utilization sensitive BOLD fMRI. In this paper, we explore the existence of state changes in electrophysiological cortical activity that can occur independently of changes in averaged amplitude, source power or indices of metabolic rates. In addition, we demonstrate that such state changes can be described by applying a new measure of complexity, rank vector entropy (RVE), to source waveform estimates from beamformer-processed MEG. RVE is a non-parametric symbolic dynamic informational entropy measure that accommodates the wide dynamic range of measured brain signals while resolving its temporal variations. By representing the measurements by their rank values, RVE overcomes the problem of defining embedding space partitions without resorting to signal compression. This renders RVE-independent of absolute signal amplitude. In addition, this approach is robust, being relatively free of tunable parameters. We present examples of task-free and task-dependent MEG demonstrating that RVE provides new information by uncovering hidden dynamical structure in the apparent turbulent (or chaotic) dynamics of spontaneous cortical activity.

Cortical oscillatory power changes during auditory oddball task revealed by spatially filtered magnetoencephalography.

OBJECTIVE: To investigate the neural sources and associated changes in oscillatory activity involved in auditory attention and memory updating processing using spatially filtered magnetoencephalography. METHODS: We recorded magnetic responses during an auditory oddball task in 12 normal subjects. Synthetic aperture magnetometry (SAM)-permutation analysis was used to visualize the multiple brain regions associated with event-related magnetic fields (ERFs), and event-related oscillations during target detection processing. RESULTS: SAM-permutation results showed the topographical distribution of N1m over the bilateral primary auditory cortex. Post-stimulus delta (1.5-4 Hz) activity sources, likely related to the P300 slow-waveform, were distributed over the right frontocentral and parietal regions. Source locations of theta (4-8 Hz) and alpha (8-13 Hz) event-related synchronization (ERS) were identified over the dorsolateral and medial prefrontal cortex. We visualized bilateral central-Rolandic suppresions for mu (8-15 Hz), beta (15-30 Hz), and low-gamma (30-60 Hz) activities, more dominant in the hemisphere contralateral to the moving hand (button-pressing in response to target stimuli). CONCLUSIONS: Prefrontal theta and alpha ERS, and frontocentral-parietal delta ERS are functionally engaged in auditory attention and memory updating process. SIGNIFICANCE: Spatially filtered MEG is valuable for detection and source localization of task-related changes in the ongoing oscillatory activity during oddball tasks.

Optimising experimental design for MEG beamformer imaging.

In recent years, the use of beamformers for source localisation has significantly improved the spatial accuracy of magnetoencephalography. In this paper, we examine techniques by which to optimise experimental design, and ensure that the application of beamformers yields accurate results. We show that variation in the experimental duration, or variation in the bandwidth of a signal of interest, can significantly affect the accuracy of a beamformer reconstruction of source power. Specifically, power will usually be underestimated if covariance windows are made too short, or bandwidths too narrow. The accuracy of spatial localisation may also be reduced. We conclude that for optimum accuracy, experimenters should aim to collect as much data as possible, and use a bandwidth spanning the entire frequency distribution of the signal of interest. This minimises distortion to reconstructed source images, time courses and power estimation. In the case where experimental duration is short, and small covariance windows are therefore used, we show that accurate power estimation can be achieved by matrix regularisation. However, large amounts of regularisation cause a loss in the spatial resolution of the MEG beamformer, hence regularisation should be used carefully, particularly if multiple sources in close proximity are expected.

Improving permutation test power for group analysis of spatially filtered MEG data.

Non-parametric statistical methods, such as permutation, are flexible tools to analyze data when the population distribution is not known. With minimal assumptions and better statistical power compared to the parametric tests, permutation tests have recently been applied to the spatially filtered magnetoencephalography (MEG) data for group analysis. To perform permutation tests on neuroimaging data, an empirical maximal null distribution has to be found, which is free from any activated voxels, to determine the threshold to classify the voxels as active at a given probability level. An iterative procedure is used to determine the distribution by computing the null distribution, which is recomputed when a possible activated voxel is found within the current distributions. Besides the high computational costs associated with this approach, there is no guarantee that all activated voxels are excluded when constructing the maximal null distribution, which may reduce the statistical power. In this study, we propose a novel way to construct the maximal null distribution from the data of the resting period. The approach is tested on the MEG data from a somatosensory experiment, and demonstrated that the approach could improve the power of the permutation test while reducing the computational cost at the same time.