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Interpreting neurophysiology recordings as a series of transient bursts with varying temporal and spectral characteristics provides meaningful insight into mechanisms underlying neural networks. Previous research has revealed age-related changes in the time-frequency dynamics of sensorimotor beta bursts, but to date, there has been little focus on the spatial localization of these beta bursts or how the localization patterns change with normal healthy ageing. The objective of the current study is to implement existing source localization algorithms for use in the detection of the cortical sources of transient beta bursts, and to uncover age-related trends in the resulting source localization patterns. Two well-established source localization algorithms (minimum-norm estimation and beamformer) were applied to localize beta bursts detected over the sensorimotor cortices in a cohort of 561 healthy participants between the ages of 18 and 88 (CamCAN open access dataset). Age-related trends were then investigated by applying regression analysis between participant age and average source power within several cortical regions of interest. This analysis revealed that beta bursts localized primarily to the sensorimotor cortex ipsilateral to the side of the sensor used for their detection. Region of interest analysis revealed that there were age-related changes in the beta burst localization pattern, with most substantial changes evidenced in frontal brain regions. In addition, regression analysis revealed a tendency of age-related trends to peak around 60 years of age suggesting that 60 is a potential critical age in this population. These results show for the first time that source localization techniques can be implemented for the identification of the sources of transient beta bursts. The exploration of these sources provides us with insight into the anatomical generators of transient beta activity and how they change across the lifespan.

OBJECTIVES Motor imagery can be used as an adjunct to traditional stroke rehabilitation therapies for individuals who have hand and arm impairment resulting from their stroke. The provision of neurofeedback during motor imagery allows individuals to receive real time information regarding their motor imagery-related brain activity. However, the equipment required to administer this feedback is expensive and largely inaccessible to many of the individuals who could benefit from it. Available EEG-based technology provides an accessible, low-cost, wireless alternative to traditional neurofeedback methods, with the tradeoff of lower gain and channel count resulting in reduced signal quality. This study investigated the efficacy of this wireless technology for the provision of motor imagery-related neurofeedback. APPROACH Twenty-eight healthy individuals participated in a 2-group, double-blinded study which involved imagining performing a unimanual button pressing task while receiving neurofeedback that is either a direct transform of their motor imagery-related brain activity (i.e., real) or is related to someone else's brain activity (i.e., sham). The change in amplitude of 15-30 Hz (beta) rhythmic brain activity elicited during the task blocks was calculated and analyzed across sessions and groups. MAIN RESULTS We found that individuals who received real neurofeedback showed a statistically significant positive trajectory in modulating the amplitude of the beta rhythm across sessions, while those who received sham feedback showed a negative trajectory. Our results did not indicate a trend of increased lateralization across sessions, as has been shown in previous studies. SIGNIFICANCE Our main findings replicated previous results with research-grade equipment indicating that there is potential for introducing this wireless technology for the provision of neurofeedback. Given the marginal longitudinal effect of neurofeedback in our study, further study is required to address the limitations associated with this technology before our protocol can be implemented in a clinical setting.

ObjectivesTo examine the association between blood-brain barrier (BBB) integrity, brain volume and cognitive dysfunction in adult patients with systemic lupus erythematosus (SLE).MethodsA total of 65 ambulatory patients with SLE and 9 healthy controls underwent dynamic contrast-enhanced MRI scanning, for quantitative assessment of BBB permeability. Volumetric data were extracted using the VolBrain pipeline. Global cognitive function was evaluated using a screening battery consisting of tasks falling into five broad cognitive domains, and was compared between patients with normal versus extensive BBB leakage.ResultsPatients with SLE had significantly higher levels of BBB leakage compared with controls (p=0.04). Extensive BBB leakage (affecting over >9% of brain volume) was identified only in patients with SLE (16/65; 24.6%), who also had smaller right and left cerebral grey matter volumes compared with controls (p=0.04). Extensive BBB leakage was associated with lower global cognitive scores (p=0.02), and with the presence of impairment on one or more cognitive tasks (p=0.01).ConclusionOur findings provide evidence for a link between extensive BBB leakage and changes in both brain structure and cognitive function in patients with SLE. Future studies should investigate the mechanisms underlying BBB-mediated cognitive impairment, validate the diagnostic utility of BBB imaging, and determine the potential of targeting the BBB as a therapeutic strategy in patients with SLE.

Objectives Magnetoencephalography (MEG) provides functional neuroimaging data for pre-surgical planning in patients with epilepsy or brain tumour. For mapping the primary somatosensory cortex (S1), MEG data are acquired while a patient undergoes median nerve stimulation (MNS) to localize components of the somatosensory evoked field (SEF). In clinical settings, only one MEG imaging session is usually possible due to limited resources. As such, it is important to have an a priori estimate of the expected variability in localization. Variability in S1 localization between mapping sessions using the same MEG system has been previously measured as 8 mm. There are different types of MEG systems available with varied hardware and software, and it is not known how using a different MEG system will impact on S1 localization. Patients and Methods In our study, healthy participants underwent the MNS procedure with two different MEG systems (Vector View and CTF). We compared the location, amplitude and latency of SEF components between data from each system to quantify variability and bias between MEG systems. Results We found 8–11 mm variability in S1 localization between the two MEG systems, and no evidence for a systematic bias in location, amplitude or latency between the two systems. Conclusion These findings suggest that S1 localization is not biased by the type of MEG system used, and that differences between the two systems are not a major contributor to variability in localization.

A discrete system&rsquo, s heterogeneity is measured by the R&eacute, nyi heterogeneity family of indices (also known as Hill numbers or Hannah&ndash, Kay indices), whose units are the numbers equivalent. Unfortunately, numbers equivalent heterogeneity measures for non-categorical data require a priori (A) categorical partitioning and (B) pairwise distance measurement on the observable data space, thereby precluding application to problems with ill-defined categories or where semantically relevant features must be learned as abstractions from some data. We thus introduce representational R&eacute, nyi heterogeneity (RRH), which transforms an observable domain onto a latent space upon which the R&eacute, nyi heterogeneity is both tractable and semantically relevant. This method requires neither a priori binning nor definition of a distance function on the observable space. We show that RRH can generalize existing biodiversity and economic equality indices. Compared with existing indices on a beta-mixture distribution, we show that RRH responds more appropriately to changes in mixture component separation and weighting. Finally, we demonstrate the measurement of RRH in a set of natural images, with respect to abstract representations learned by a deep neural network. The RRH approach will further enable heterogeneity measurement in disciplines whose data do not easily conform to the assumptions of existing indices.

Non-invasive neurophysiological recordings, such as those measured by magnetoencelography (MEG), provide insight into the behaviour of neural networks and how these networks change with factors such as task performance, disease state, and age. Recently, there has been a trend in describing neurophysiological recordings as a series of transient bursts of neural activity rather than averaged sustained oscillations as burst characteristics may be more directly correlated with the neurological generators of brain activity. In this work, we investigate how beta burst characteristics change with age in a large open access dataset. The objectives are (1) to detect and characterize transient beta bursts over the ipsilateral and contralateral primary sensorimotor cortices during a unilateral motor task performance and during wakeful resting, and (2) to identify age-related changes in beta burst characteristics, in the context of earlier reports of age-related changes in beta suppression and the post-movement beta rebound. MEG data, acquired at the Cambridge Centre for Ageing and Neuroscience, of roughly 600 participants with a nearly uniform distribution of ages between 18 and 88 years old was used for analysis. We found that burst rate is the predominant factor related to age-related changes in the amplitude of the induced beta rhythm responses associated with a button press task. Furthermore, we present a cross-validation of burst parameters detected at the sensor- (peak sensor and sensor ROI) and source-level (beamformer spatial filter). This work is as an important step in characterizing transient bursts in neuromagnetic signals in the temporal domain, towards a better understanding of the healthy aging human brain.

Convolutional neural networks were used to classify and analyse a large magnetoencephalography (MEG) dataset. Networks were trained to classify between active and baseline intervals of minimally-processed data recorded during cued button pressing. There were two primary objectives for this study: (1) develop networks that can effectively classify MEG data, and (2) identify the important data features that inform classification. Networks with a simple architecture were trained using sensor and source-localised data. Networks trained with sensor data were also trained using varying amounts of data. The important features within the data were identified via saliency and occlusion mapping. An ensemble of networks trained using sensor data performed best (average test accuracy 0.974 ± 0.001). A dataset containing on the order of hundreds of participants was required for optimal performance of this network with these data. Visualisation maps highlighted features known to occur during neuromagnetic recordings of cued button pressing.

BACKGROUNDIn late 2016, US diplomats stationed in Havana began presenting with a variety of neurological manifestations that proved difficult to diagnose. Though previous studies suggested a likely association with brain injury, the mechanism of injury, brain regions involved, and etiology remained unknown.METHODSWe conducted a multimodal study examining 26 Canadian diplomats and their family members, the majority of whom presented with symptoms similar to their American counterparts while residing in Havana. Assessments included a medical history, self-reported symptom questionnaires, cognitive assessments, blood tests, and brain imaging assessments (magnetic resonance imaging (MRI) and magnetoencephalography (MEG)). Individuals showing signs of brain injury underwent further neurological, visual, and audio-vestibular assessments. Eight participants were tested both before and after living in Havana.RESULTSOur assessment documents multiple functional and structural impairments, including significant spatial memory impairment, abnormal brain-stem evoked potentials, degradation of fibre tracts in the fornix and posterior corpus callosum, blood-brain barrier injury to the right basal forebrain and anterior insula, and abnormal paroxysmal slowing events of cortical activity. Subsequent mass-spectrometry and blood analyses documented reduced serum cholinesterase activity and the presence of organophosphates (Temephos) and pyrethroid metabolites (3-phenoxybenzoic acid or 3-BPA).CONCLUSIONSOur findings confirm brain injury, specify the regions involved, and raise the hypothesis of overexposure to cholinesterase inhibitors as a plausible etiology. If correct, our hypothesis bears public health ramifications (see Discussion) and suggests a course of action for reducing exposure in the future.FUNDINGGlobal Affairs Canada.

Objectives We estimated the cost-effectiveness of adding magnetoencephalography to a standard assessment for epilepsy surgery consisting of neuropsychology, magnetic resonance imagining, scalp electroencephalography, video electroencephalography and intracranial electroencephalography, in the capacity of informing intracranial electroencephalography electrode placement. Methods We used Microsoft Excel (2007) to construct a decision model. Discounted costs and quality adjusted life years are aggregated to calculate incremental cost-effectiveness ratios. Sensitivity analyses are conducted to assess robustness of findings. Results Our base case analysis yielded a result of $14 300 per quality adjusted life year gained. A total of 82.7% of probabilistic sensitivity analysis iterations resulted in incremental cost-effectiveness ratios below $100 000 in 2014 Canadian dollars. Conclusions Our findings demonstrate that the inclusion of Magnetoencephalography in the assessment for epilepsy surgery in the capacity of informing intracranial electroencephalography electrode placement is likely not cost saving but does represent a reasonable allocation of resources from a value for money perspective.

A large literature documenting facilitative effects for high frequency complex words and phrases has led to proposals that high frequency phrases may be stored in memory rather than constructed on-line from their component parts (similarly to high frequency complex words). To investigate this, we explored language processing during a novel picture description task. Using the magneto-encephalographam (MEG) technique and generalised additive mixed-effects modelling, we characterised the effects of the frequency of use of single words as well as two-, three-, and four-word sequences (N-grams) on brain activity during the pre-production stage of unconstrained overt picture description. We expected amplitude responses to be modulated by N-gram frequency such that if N-grams were stored we would see a corresponding reduction or flattening in amplitudes as frequency increased. We found that while amplitude responses to increasing N-gram frequencies corresponded with our expectations about facilitation, the effect appeared at low frequency ranges and for single words only in the phonological network. We additionally found that high frequency N-grams elicited activity increases in some networks, which may be signs of competition or combination depending on the network. Moreover, this effect was not reliable for single word frequencies. These amplitude responses do not clearly support storage for high frequency multi-word sequences. To probe these unexpected results, we turned our attention to network topographies and the timing. We found that, with the exception of an initial ‘sentence’ network, all the networks aggregated peaks from more than one domain (e.g. semantics and phonology). Moreover, although activity moved serially from anterior ventral networks to dorsal posterior networks during processing, as expected in combinatorial accounts, sentence processing and semantic networks ran largely in parallel. Thus, network topographies and timing may account for (some) facilitative effects associated with frequency. We review literature relevant to the network topographies and timing and briefly discuss our results in relation to current processing and theoretical models.

We thank Dr Pamuk et al 1 for their interest in our work,2 and for providing insights regarding mechanisms that may help explain our findings. Their correspondence highlights the importance of further understanding the processes underlying our main finding: extensive blood-brain barrier (BBB) leakage affects ~25% of patients with systemic lupus erythematosus (SLE) and is linked to smaller grey matter volume and cognitive impairment. We agree that the mechanisms resulting in BBB leakage in SLE remain poorly understood, and that …

Objectives When using MEG for pre-surgical mapping it is critically important that reliable estimates of functional locations, such as the primary visual cortex (V1) can be provided. Several different models of MEG systems exist, each with varying software and hardware configurations, and it is not currently known how the system type contributes to variability in V1 localization. Patients and Methods In this study, participants underwent MEG sessions using two different systems (Vector View and CTF) during which they were presented with a repeating grating stimulus to the lower-left visual quadrant to generate a visual evoked field (VEF). The location, amplitude and latency of the VEF source was compared between systems for each participant. Results No significant differences were found in latency and amplitude between systems, however, a significant bias in the latero-medial position of the localization was present. The median inter-system Euclidian distance between V1 localization across participants was 10.5 mm. Conclusions Overall, our results indicate that mapping of V1 can be reliably reproduced within approximately one centimetre by different MEG systems. Significance This result provides knowledge of the useful limits on the reliability of localization which can be taken into consideration in clinical practice.

Mu, beta, and gamma rhythms increase and decrease in amplitude during movement. This event-related synchronization (ERS) and desynchronization (ERD) can be readily recorded non-invasively using magneto- and electro-encephalography (M/EEG). In addition, event-related potentials and fields (i.e., evoked responses) can be elucidated during movement. There is some evidence that the frequency, amplitude and latency of the movement-related ERS/ERD changes with ageing, however the evidence surrounding this topic comes mainly from studies in sample sizes on the order of tens of participants. The objective of this study was to examine a large open-access MEG dataset for age-related changes in movement-related ERS/ERD and evoked responses. MEG data acquired at the Cambridge Centre for Ageing and Neuroscience during cued button pressing was used from 567 participants between the ages of 18 and 88 years. The characteristics movement-related ERD/ERS and evoked responses were calculated for each individual participant. Based on linear regression analysis, significant relationships were found between participant age and some response characteristics, although the predictive value of these relationships was low. Specifically, we conclude that peak beta rebound frequency and amplitude decreased with age, peak beta suppression amplitude increased with age, movement-related gamma burst amplitude decreased with age, and peak motor-evoked response amplitude increased with age. Given our current understanding of the underlying mechanisms of these responses, our findings suggest the existence of age-related changes in the neurophysiology of thalamocortical loops and local circuitry in the primary somatosensory and motor cortices.

Functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) are neuroimaging techniques that measure inherently different physiological processes, resulting in complementary estimates of brain activity in different regions. Combining the maps generated by each technique could thus provide a richer understanding of brain activation. However, present approaches to integration rely on a priori assumptions, such as expected patterns of brain activation in a task, or use fMRI to bias localization of MEG sources, diminishing fMRI-invisible sources. We aimed to optimize sensitivity to neural activity by developing a novel method of integrating data from the two imaging techniques. We present a data-driven method of integration that weights fMRI and MEG imaging data by estimates of data quality for each technique and region. This method was applied to a verbal object recognition task. As predicted, the two imaging techniques demonstrated sensitivity to activation in different regions. Activity was seen using fMRI, but not MEG, throughout the medial temporal lobes. Conversely, activation was seen using MEG, but not fMRI, in more lateral and anterior temporal lobe regions. Both imaging techniques were sensitive to activation in the inferior frontal gyrus. Importantly, integration maps retained activation from individual activation maps, and showed an increase in the extent of activation, owing to greater sensitivity of the integration map than either fMRI or MEG alone.

To examine the effect of concussion on indices of attention using magnetoencephalography.Thirteen patients were recruited from the emergency department and scanned within 3-6 days of injury. Five returned for follow-up scans one and three months post-injury. Thirteen healthy controls also completed testing. During MEG acquisition, participants performed the Attention Network Test (ANT). Cognitive evoked responses to this task include a cue-evoked P300m, a contingent magnetic variation (CMV) and a target-evoked P300m. The Rivermead Postconcussion Symptom Questionnaire and Sport Concussion Assessment Tool (SCAT3) were administered in all sessions.Patients suffering from concussion had slower response times and benefitted more from spatial cues than did controls. Global activation for all three evoked responses was lower for patients than controls. In a small sample of patients who returned for follow-up, the CMV and target P300m improved with recovery.MEG-evoked responses to the ANT reveal neurophysiological evidence of attentional dysfunction within days of injury. A pattern of improvement was also observed over the course of three months for the P300m, while behavioural performance did not change significantly. Further development of this method may yield a useful adjunct to neurological examination for concussion diagnosis and monitoring.

Motor imagery (MI) is a form of practice in which an individual mentally performs a motor task. Previous research suggests that skill acquisition via MI is facilitated by repetitive activation of brain regions in the sensorimotor network similar to that of motor execution, however this evidence is conflicting. Further, many studies do not control for overt muscle activity and thus the activation patterns reported for MI may be driven in part by actual movement. The purpose of the current research is to further establish MI as a secondary modality of skill acquisition by providing electrophysiological evidence of an overlap between brain areas recruited for motor execution and imagery. Non-disabled participants (N=18; 24.7±3.8 years) performed both execution and imagery of a unilateral sequence button-press task. Magnetoencephalography (MEG) was utilized to capture neural activity, while electromyography used to rigorously monitor muscle activity. Event-related synchronization/desynchronization (ERS/ERD) analysis was conducted in the beta frequency band (15-30 Hz). Whole head dual-state beamformer analysis was applied to MEG data and 3D t-tests were conducted after Talairach normalization. Source-level analysis showed that MI has similar patterns of spatial activity as ME, including activation of contralateral primary motor and somatosensory cortices. However, this activation is significantly less intense during MI (p

Neurofeedback- and brain-computer interface (BCI)-based interventions can be implemented using real-time analysis of magnetoencephalographic (MEG) recordings. Head movement during MEG recordings, however, can lead to inaccurate estimates of brain activity, reducing the efficacy of the intervention. Most real-time applications in MEG have utilized analyses that do not correct for head movement. Effective means of correcting for head movement are needed to optimize the use of MEG in such applications. Here we provide preliminary validation of a novel analysis technique, real-time source estimation (rtSE), that measures head movement and generates corrected current source time course estimates in real-time. rtSE was applied while recording a calibrated phantom to determine phantom position localization accuracy and source amplitude estimation accuracy under stationary and moving conditions. Results were compared to off-line analysis methods to assess validity of the rtSE technique. The rtSE method allowed for accurate estimation of current source activity at the source-level in real-time, and accounted for movement of the source due to changes in phantom position. The rtSE technique requires modifications and specialized analysis of the following MEG work flow steps. • Data acquisition • Head position estimation • Source localization • Real-time source estimation This work explains the technical details and validates each of these steps.

Motor imagery (MI) and action observation have proven to be efficacious adjuncts to traditional physiotherapy for enhancing motor recovery following stroke. Recently, researchers have used a combined approach called imagined imitation (II), where an individual watches a motor task being performed, while simultaneously imagining they are performing the movement. While neurofeedback (NFB) has been used extensively with MI to improve patients' ability to modulate sensorimotor activity and enhance motor recovery, the effectiveness of using NFB with II to modulate brain activity is unknown. This project tested the ability of participants to modulate sensorimotor activity during electroencephalography-based II-NFB of a complex, multi-part unilateral handshake, and whether this ability transferred to a subsequent bout of MI. Moreover, given the goal of translating findings from NFB research into practical applications, such as rehabilitation, the II-NFB system was designed with several user interface and user experience features, in an attempt to both drive user engagement and match the level of challenge to the abilities of the subjects. In particular, at easy difficulty levels the II-NFB system incentivized contralateral sensorimotor up-regulation (via event related desynchronization of the mu rhythm), while at higher difficulty levels the II-NFB system incentivized sensorimotor lateralization (i.e., both contralateral up-regulation and ipsilateral down-regulation). Thirty-two subjects, receiving real or sham NFB attended four sessions where they engaged in II-NFB training and subsequent MI. Results showed the NFB group demonstrated more bilateral sensorimotor activity during sessions 2–4 during II-NFB and subsequent MI, indicating mixed success for the implementation of this particular II-NFB system. Here we discuss our findings in the context of the design features included in the II-NFB system, highlighting limitations that should be considered in future designs.

Neurofeedback training teaches individuals to modulate brain activity by providing real-time feedback and can be used for brain–computer interface control. The present study aimed to optimize training by maximizing engagement through goal-oriented task design. Participants were shown either a visual display or a robot, where each was manipulated using motor imagery (MI)-related electroencephalography signals. Those with the robot were instructed to quickly navigate grid spaces, as the potential for goal-oriented design to strengthen learning was central to our investigation. Both groups were hypothesized to show increased magnitude of these signals across 10 sessions, with the greatest gains being seen in those navigating the robot due to increased engagement. Participants demonstrated the predicted increase in magnitude, with no differentiation between hemispheres. Participants navigating the robot showed stronger left-hand MI increases than those with the computer display. This is likely due to success being reliant on maintaining strong MI-related signals. While older participants showed stronger signals in early sessions, this trend later reversed, suggesting greater natural proficiency but reduced flexibility. These results demonstrate capacity for modulating neurofeedback using MI over a series of training sessions, using tasks of varied design. Importantly, the more goal-oriented robot control task resulted in greater improvements.

Functional imaging is increasingly being used to provide a noninvasive alternative to intracarotid sodium amobarbitol testing (i.e., the Wada test). Although magnetoencephalography (MEG) has shown significant potential in this regard, the resultant output is often reduced to a simplified estimate of laterality. Such estimates belie the richness of functional imaging data and consequently limit the potential value. We present a novel approach that utilizes MEG data to compute “complex laterality vectors” and consequently “laterality maps” for a given function. Language function was examined in healthy controls and in people with epilepsy. When compared with traditional laterality index (LI) approaches, the resultant maps provided critical information about the magnitude and spatial characteristics of lateralized function. Specifically, it was possible to more clearly define low LI scores resulting from strong bilateral activation, high LI scores resulting from weak unilateral activation, and most importantly, the spatial distribution of lateralized activation. We argue that the laterality concept is better presented with the inherent spatial sensitivity of activation maps, rather than being collapsed into a one‐dimensional index. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc.

In noisy social gatherings, listeners perceptually integrate sounds originating from one person's voice (e.g., fundamental frequency (f(0)) and harmonics) at a particular location and segregate these from concurrent sounds of other talkers. Though increasing the spectral or the spatial distance between talkers promotes speech segregation, synergetic effects of spatial and spectral distances are less well understood. We studied how spectral and/or spatial distances between 2 simultaneously presented steady-state vowels contribute to perception and activation in auditory cortex using magnetoencephalography. Participants were more accurate in identifying both vowels when they differed in f(0) and location than when they differed in a single cue only or when they shared the same f(0) and location. The combined effect of f(0) and location differences closely matched the sum of single effects. The improvement in concurrent vowel identification coincided with an object-related negativity that peaked at about 140 ms after vowel onset. The combined effect of f(0) and location closely matched the sum of the single effects even though vowels with different f(0), location, or both generated different time courses of neuromagnetic activity. We propose that during auditory scene analysis, acoustic differences among the various sources are combined linearly to increase the perceptual distance between the co-occurring sound objects.

Robust and reproducible source mapping with magnetoencephalography is particularly challenging at the individual level. We evaluated a receiver-operating characteristic reliability (ROC-r) method for automated production of volumetric MEG maps in single-subjects. ROC-r provides quality assurance comparable to that offered by goodness-of-fit (GoF) and confidence volume (CV) for equivalent current dipole (ECD) modeling.ROC-r utilizes within-session reproducibility for quality assurance, latency identification, and thresholding of volumetric source maps. We tested ROC-r on simulated and real MEG with a strongly focal source, using somatosensory evoked fields (SEFs) elicited by bilateral median nerve stimulation (MNS). For quality assurance, the ROC-r reliable fraction (FR) was compared to the ECD GoF and CV. Peak beamformer locations and latencies identified by ROC-r were compared to the ECD for co-localization accuracy.The predominant component of the SEF response occurred around 35ms, contralateral to the MNS.FR and 1/CV were more strongly correlated (mean Pearson's correlation: 0.76; 95% CI 0.60-0.87) than FR and GoF (0.65; 95% CI 0.32-0.85). There was no difference in the latency of the peak GoF (35.0+/-0.6ms), CV (34.8+/-0.7ms) and FR (35.5+/-0.8ms). The ECD fits and ROC-r peaks co-localized to within a mean (median) distance of 8.3+/-5.9mm (6.2mm).ROC-r volumetric mapping co-localized closely with the standard ECD approach. This analysis can be added to any whole-brain MEG source imaging protocol, and is especially useful for single-subject mapping. Additionally, the development of FR as an analogue to GoF or CV for volumetric mapping is a critical improvement for clinical applications.

Objectives In patients with epilepsy or space occupying tumors in cortical regions, surgical resection is often considered as the primary treatment. Pre-surgical neuroimaging can provide a detailed map of pathological and functional cortex, leading to safer surgery. Mapping can be achieved non-invasively using magnetoencephalography (MEG), and is concordant with invasive findings. However, the reliability of MEG mapping between sessions is not well established. The inter-session reliability is an important property in pre-surgical mapping to establish resection margins, but repeated scans are impracticable. The present study sought to quantify the intersession reliability of MEG localization of somatosensory cortex (S1). Patients and methods Eighteen healthy individuals underwent MEG sessions on 3 consecutive days. Five participants were excluded due to technical issues during one of the three days. Each session included clinical-style S1 localization using electrical stimuli to each median nerve at sub-motor thresholds. The 35 ms peak of the somatosensory evoked field was used for localizing S1 in each session using a single equivalent current dipole model. Intersession reliability was quantified using two methods. Average Euclidean Distance (AED) quantified the difference in localization between each session and the inter-session mean localization. Session Euclidean Distance (SED) quantified the difference in localization between each pair of sessions. Results and discussion Results showed the AED was 4.8 ± 1.9 mm, whereas the SED was 8.3 ± 3.4 mm. While the AED values obtained parallel those reported previously in smaller samples, the SED values were substantially larger. Conclusion Clinicians should consider up to an 8 mm confidence interval around the estimated location of S1 based on MEG pre-surgical mapping.

Accuracy in localizing the brain areas that generate neuromagnetic activity in magnetoencephalography (MEG) is dependent on properly co-registering MEG data to the participant’s structural magnetic resonance image (MRI). Effective MEG-MRI co-registration is, in turn, dependent on how accurately we can digitize anatomical landmarks on the surface of the head. In this study, we compared the performance of three devices —Polhemus electromagnetic system, NextEngine laser scanner and Microsoft Kinect for Windows — for source localization accuracy and MEG-MRI co-registration. A calibrated phantom was used for verifying the source localization accuracy. The Kinect improved source localization accuracy over the Polhemus and the laser scanner by 2.23 mm (137%) and 0.81 mm (50%), respectively. MEG-MRI co-registration accuracy was verified on data from five healthy human participants, who received the digitization process using all three devices. The Kinect device captured approximately 2000 times more surface points than the Polhemus in one third of the time (one minute compared to three minutes) and thrice as many points as the NextEngine laser scanner. Following automated surface matching, the calculated mean MEG-MRI co-registration error for the Kinect was improved by 2.85 mm with respect to the Polhemus device, and equivalent to the laser scanner. Importantly, the Kinect device automatically aligns 20-30 images per second in real-time, reducing the limitations on participant head movement during digitization that are implicit in NextEngine laser scan (~1 minute). We conclude that the Kinect scanner is an effective device for head digitization in MEG, providing the necessary accuracy in source localization and MEG-MRI co-registration, while reducing digitization time.

Brain source localization accuracy in magnetoencephalography (MEG) requires accuracy in both digitizing anatomical landmarks and coregistering to anatomical magnetic resonance images (MRI). We compared the source localization accuracy and MEG-MRI coregistration accuracy of two head digitization systems - a laser scanner and the current standard electromagnetic digitization system (Polhemus) - using a calibrated phantom and human data. When compared using the calibrated phantom, surface and source localization accuracy for data acquired with the laser scanner improved over the Polhemus by 141% and 132%, respectively. Laser scan digitization reduced MEG source localization error by 1.38 mm on average. In human participants, a laser scan of the face generated a 1000-fold more points per unit time than the Polhemus head digitization. An automated surface-matching algorithm improved the accuracy of MEG-MRI coregistration over the equivalent manual procedure. Simulations showed that the laser scan coverage could be reduced to an area around the eyes only while maintaining coregistration accuracy, suggesting that acquisition time can be substantially reduced. Our results show that the laser scanner can both reduce setup time and improve localization accuracy, in comparison to the Polhemus digitization system.

Although an object-based account of auditory attention has become an increasingly popular model for understanding how temporally overlapping sounds are segregated, relatively little is known about the cortical circuit that supports such ability. In the present study, we applied a beamformer spatial filter to magnetoencephalography (MEG) data recorded during an auditory paradigm that used inharmonicity to promote the formation of multiple auditory objects. Using this unconstrained, data-driven approach, the evoked field component linked with the perception of multiple auditory objects (i.e., the object-related negativity; ORNm), was found to be associated with bilateral auditory cortex sources that were distinct from those coinciding with the P1m, N1m, and P2m responses elicited by sound onset. The right hemispheric ORNm source in particular was consistently positioned anterior to the other sources across two experiments. These findings are consistent with earlier proposals of multiple auditory object detection being associated with generators in the auditory cortex and further suggest that these neural populations are distinct from the long latency evoked responses reflecting the detection of sound onset.

Neuropsychological and neuroimaging findings reveal that the hippocampus is important for recognition memory. However, it is unclear when and whether the hippocampus contributes differentially to recognition of previously studied items (old) versus novel items (new), or contributes to a general processing requirement that is necessary for recognition of both types of information. To address this issue, we examined the temporal dynamics and spectral frequency underlying hippocampal activity during recognition of old/new complex scenes using magnetoencephalography (MEG). In order to provide converging evidence to existing literature in support of the potential of MEG to localize the hippocampus, we reconstructed brain source activity using the beamformer method and analyzed three types of processing-related signal changes by applying three different analysis methods: (1) Synthetic aperture magnetometry (SAM) revealed event related and non-event-related spectral power changes; (2) Inter-trial coherence (ITC) revealed time-locked changes in neural synchrony; and (3) Event-related SAM (ER-SAM) revealed averaged event-related responses over time. Hippocampal activity was evident for both old and new information within the theta frequency band and during the first 250 ms following stimulus onset. The early onset of hippocampal responses suggests that general comparison processes related to recognition of new/old information may occur obligatorily.