Modified beamformers for coherent source region suppression

Regular cannabis use modulates gamma activity in brain regions serving motor control

BACKGROUND

People who regularly use cannabis exhibit altered brain dynamics during cognitive control tasks, though the impact of regular cannabis use on the neural dynamics serving motor control remains less understood.

AIMS

We sought to investigate how regular cannabis use modulates the neural dynamics serving motor control.

METHODS

Thirty-four people who regularly use cannabis (cannabis+) and 33 nonusers (cannabis-) underwent structured interviews about their substance use history and performed the Eriksen flanker task to map the neural dynamics serving motor control during high-density magnetoencephalography (MEG). The resulting neural data were transformed into the time-frequency domain to examine oscillatory activity and were imaged using a beamforming approach.

RESULTS

MEG sensor-level analyses revealed robust beta (16-24 Hz) and gamma oscillations (66-74 Hz) during motor planning and execution, which were imaged using a beamformer. Both responses peaked in the left primary motor cortex and voxel time series were extracted to evaluate the spontaneous and oscillatory dynamics. Our key findings indicated that the cannabis+ group exhibited weaker spontaneous gamma activity in the left primary motor cortex relative to the cannabis- group, which scaled with cannabis use and behavioral metrics. Interestingly, regular cannabis use was not associated with differences in oscillatory beta and gamma activity, and there were no group differences in spontaneous beta activity.

CONCLUSIONS

Our findings suggest that regular cannabis use is associated with suppressed spontaneous gamma activity in the left primary motor cortex, which scales with the degree of cannabis use disorder symptomatology and is coupled to behavioral task performance.

People with HIV exhibit spectrally distinct patterns of rhythmic cortical activity serving cognitive flexibility

Despite effective antiretroviral therapy, cognitive impairment remains prevalent among people with HIV (PWH) and decrements in executive function are particularly prominent. One component of executive function is cognitive flexibility, which integrates a variety of executive functions to dynamically adapt one's behavior in response to changing contextual demands. Though substantial work has illuminated HIV-related aberrations in brain function, it remains unclear how the neural oscillatory dynamics serving cognitive flexibility are affected by HIV-related alterations in neural functioning. Herein, 149 participants (PWH: 74; seronegative controls: 75) between the ages of 29-76 years completed a perceptual feature matching task that probes cognitive flexibility during high-density magnetoencephalography (MEG). Neural responses were decomposed into the time-frequency domain and significant oscillatory responses in the theta (4-8 Hz), alpha (10-16 Hz), and gamma (74-98 Hz) spectral windows were imaged using a beamforming approach. Whole-brain voxel-wise comparisons were then conducted on these dynamic functional maps to identify HIV-related differences in the neural oscillatory dynamics supporting cognitive flexibility. Our findings indicated group differences in alpha oscillatory activity in the cingulo-opercular cortices, and differences in gamma activity were found in the cerebellum. Across all participants, alpha and gamma activity in these regions were associated with performance on the cognitive flexibility task. Further, PWH who had been treated with antiretroviral therapy for a longer duration and those with higher current CD4 counts had alpha responses that more closely resembled those of seronegative controls, suggesting that optimal clinical management of HIV infection is associated with preserved neural dynamics supporting cognitive flexibility.

Interplay between preclinical indices of obesity and neural signatures of fluid intelligence in youth

Pediatric obesity rates have quadrupled in the United States, and deficits in higher-order cognition have been linked to obesity, though it remains poorly understood how deviations from normal body mass are related to the neural dynamics serving cognition in youth. Herein, we determine how age- and sex-adjusted measures of body mass index (zBMI) scale with neural activity in brain regions underlying fluid intelligence. Seventy-two youth aged 9-16 years underwent high-density magnetoencephalography while performing an abstract reasoning task. The resulting data were transformed into the time-frequency domain and significant oscillatory responses were imaged using a beamformer. Whole-brain correlations with zBMI were subsequently conducted to quantify relationships between zBMI and neural activity serving abstract reasoning. Our results reveal that participants with higher zBMI exhibit attenuated theta (4-8 Hz) responses in both the left dorsolateral prefrontal cortex and left temporoparietal junction, and that weaker temporoparietal responses scale with slower reaction times. These findings suggest that higher zBMI values are associated with weaker theta oscillations in key brain regions and altered performance during an abstract reasoning task. Thus, future investigations should evaluate neurobehavioral function during abstract reasoning in youth with more severe obesity to identify the potential impact.

Chronic cannabis use alters the spontaneous and oscillatory gamma dynamics serving cognitive control

Regular cannabis use is associated with cortex-wide changes in spontaneous and oscillatory activity, although the functional significance of such changes remains unclear. We hypothesized that regular cannabis use would suppress spontaneous gamma activity in regions serving cognitive control and scale with task performance. Participants (34 cannabis users, 33 nonusers) underwent an interview regarding their substance use history and completed the Eriksen flanker task during magnetoencephalography (MEG). MEG data were imaged in the time-frequency domain and virtual sensors were extracted from the peak voxels of the grand-averaged oscillatory interference maps to quantify spontaneous gamma activity during the pre-stimulus baseline period. We then assessed group-level differences in spontaneous and oscillatory gamma activity, and their relationship with task performance and cannabis use metrics. Both groups exhibited a significant behavioral flanker interference effect, with slower responses during incongruent relative to congruent trials. Mixed-model ANOVAs indicated significant gamma-frequency neural interference effects in the left frontal eye fields (FEF) and left temporoparietal junction (TPJ). Further, a group-by-condition interaction was detected in the left FEF, with nonusers exhibiting stronger gamma oscillations during incongruent relative to congruent trials and cannabis users showing no difference. In addition, spontaneous gamma activity was sharply suppressed in cannabis users relative to nonusers in the left FEF and TPJ. Finally, spontaneous gamma activity in the left FEF and TPJ was associated with task performance across all participants, and greater cannabis use was associated with weaker spontaneous gamma activity in the left TPJ of the cannabis users. Regular cannabis use was associated with weaker spontaneous gamma in the TPJ and FEF. Further, the degree of use may be proportionally related to the degree of suppression in spontaneous activity in the left TPJ.

Chronic Cannabis users exhibit altered oscillatory dynamics and functional connectivity serving visuospatial processing

BACKGROUND

Cannabis is the most widely used psychoactive drug in the United States. While multiple studies have associated acute cannabis consumption with alterations in cognitive function (e.g., visual and spatial attention), far less is known regarding the effects of chronic consumption on the neural dynamics supporting these cognitive functions.

METHODS

We used magnetoencephalography (MEG) and an established visuospatial processing task to elicit multi-spectral neuronal responses in 44 regular cannabis users and 53 demographically matched non-user controls. To examine the effects of chronic cannabis use on the oscillatory dynamics underlying visuospatial processing, neural responses were imaged using a time-frequency resolved beamformer and compared across groups.

RESULTS

Neuronal oscillations serving visuospatial processing were identified in the theta (4-8 Hz), alpha (8-14 Hz), and gamma range (56-76 Hz), and these were imaged and examined for group differences. Our key results indicated that users exhibited weaker theta oscillations in occipital and cerebellar regions and weaker gamma responses in the left temporal cortices compared to non-users. Lastly, alpha oscillations did not differ, but alpha connectivity among higher-order attention areas was weaker in cannabis users relative to non-users and correlated with performance.

CONCLUSIONS

Overall, these results suggest that chronic cannabis users have alterations in the oscillatory dynamics and neural connectivity serving visuospatial attention. Such alterations were observed across multiple cortical areas critical for higher-order processing and may reflect compensatory activity and/or the initial emergence of aberrant dynamics. Future work is needed to fully understand the implications of altered multispectral oscillations and neural connectivity in cannabis users.

Optimal gamma-band entrainment of visual cortex

Visual entrainment is a powerful and widely used research tool to study visual information processing in the brain. While many entrainment studies have focused on frequencies around 14-16 Hz, there is renewed interest in understanding visual entrainment at higher frequencies (e.g., gamma-band entrainment). Notably, recent groundbreaking studies have demonstrated that gamma-band visual entrainment at 40 Hz may have therapeutic effects in the context of Alzheimer's disease (AD) by stimulating specific neural ensembles, which utilize GABAergic signaling. Despite such promising findings, few studies have investigated the optimal parameters for gamma-band visual entrainment. Herein, we examined whether visual stimulation at 32, 40, or 48 Hz produces optimal visual entrainment responses using high-density magnetoencephalography (MEG). Our results indicated strong entrainment responses localizing to the primary visual cortex in each condition. Entrainment responses were stronger for 32 and 40 Hz relative to 48 Hz, indicating more robust synchronization of neural ensembles at these lower gamma-band frequencies. In addition, 32 and 40 Hz entrainment responses showed typical patterns of habituation across trials, but this effect was absent for 48 Hz. Finally, connectivity between visual cortex and parietal and prefrontal cortices tended to be strongest for 40 relative to 32 and 48 Hz entrainment. These results suggest that neural ensembles in the visual cortex may resonate at around 32 and 40 Hz and thus entrain more readily to photic stimulation at these frequencies. Emerging AD therapies, which have focused on 40 Hz entrainment to date, may be more effective at lower relative to higher gamma frequencies, although additional work in clinical populations is needed to confirm these findings. PRACTITIONER POINTS: Gamma-band visual entrainment has emerged as a therapeutic approach for eliminating amyloid in Alzheimer's disease, but its optimal parameters are unknown. We found stronger entrainment at 32 and 40 Hz compared to 48 Hz, suggesting neural ensembles prefer to resonate around these relatively lower gamma-band frequencies. These findings may inform the development and refinement of innovative AD therapies and the study of GABAergic visual cortical functions.

Altered long-range functional connectivity in PTSD: Role of the infraslow oscillations of cortical activity amplitude envelopes

OBJECTIVE

Coupling between the amplitude envelopes (AEs) of regional cortical activity reflects mechanisms that coordinate the excitability of large-scale cortical networks. We used resting-state MEG recordings to investigate the association between alterations in the coupling of cortical AEs and symptoms of post-traumatic stress disorder (PTSD).

METHODS

Participants (n = 96) were service members with combat exposure and various levels of post-traumatic stress severity (PTSS). We assessed the correlation between PTSS and (1) coupling of broadband cortical AEs of beta band activity, (2) coupling of the low- (<0.5 Hz) and high-frequency (>0.5 Hz) components of the AEs, and (3) their time-varying patterns.

RESULTS

PTSS was associated with widespread hypoconnectivity assessed from the broadband AE fluctuations, which correlated with subscores for the negative thoughts and feelings/emotional numbing (NTF/EN) and hyperarousal clusters of symptoms. Higher NTF/EN scores were also associated with smaller increases in resting-state functional connectivity (rsFC) with time during the recordings. The distinct patterns of rsFC in PTSD were primarily due to differences in the coupling of low-frequency (infraslow) fluctuations of the AEs of beta band activity.

CONCLUSIONS

Our findings implicate the mechanisms underlying the regulation/coupling of infraslow oscillations in the alterations of rsFC assessed from broadband AEs and in PTSD symptomatology.

SIGNIFICANCE

Altered coordination of infraslow amplitude fluctuations across large-scale cortical networks can contribute to network dysfunction and may provide a target for treatment in PTSD.

The neural oscillations serving task switching are altered in cannabis users

BACKGROUND

Regular cannabis is known to impact higher-order cognitive processes such as attention, but far less is known regarding cognitive flexibility, a component of executive function. Moreover, whether such changes are related to aberrations in the neural oscillatory dynamics serving flexibility remains poorly understood.

AIMS

Quantify the neural oscillatory dynamics serving cognitive flexibility by having participants complete a task-switching paradigm during magnetoencephalography (MEG). Probe whole-brain maps to identify alterations in chronic cannabis users relative to nonusers and determine how these alterations relate to the degree of cannabis use involvement.

METHODS

In all, 25 chronic cannabis users and 30 demographically matched nonuser controls completed neuropsychological testing, an interview regarding their substance use, a urinalysis, and a task switch paradigm during MEG. Time-frequency windows of interest were identified using a data-driven statistical approach and these were imaged using a beamformer. Whole-brain neural switch cost maps were computed by subtracting the oscillatory maps of the no-switch condition from the switch condition per participant. These were examined for group differences.

RESULTS

Cannabis users had weaker theta switch cost responses in the dorsolateral and dorsomedial prefrontal cortices, while nonusers showed the typical pattern of greater recruitment during switch relative to no switch trials. In addition, theta activity in the dorsomedial prefrontal cortex was significantly correlated with cannabis use involvement.

CONCLUSIONS

Cannabis users exhibited altered theta switch cost activity compared to nonusers in prefrontal cortical regions, which are critical for cognitive flexibility. This activity scaled with cannabis use involvement, indicating a link between cannabis use and aberrant oscillatory activity underlying cognitive flexibility.

Localization of Brain Signals by Alternating Projection

A popular approach for modeling brain activity in MEG and EEG is based on a small set of current dipoles, where each dipole represents the combined activation of a local area of the brain. Here, we address the problem of multiple dipole localization with a novel solution called Alternating Projection (AP). The AP solution is based on minimizing the least-squares (LS) criterion by transforming the multi-dimensional optimization required for direct LS solution, to a sequential and iterative solution in which one source at a time is localized, while keeping the other sources fixed. Results from simulated, phantom, and human MEG data demonstrated the high accuracy of the AP method, with superior localization results than popular scanning methods from the multiple-signal classification (MUSIC) and beamformer families. In addition, the AP method was more robust to forward model errors resulting from head rotations and translations, as well as different cortex tessellation grids for the forward and inverse solutions, with consistently higher localization accuracy in low SNR and highly correlated sources.

Healthy aging alters the oscillatory dynamics and fronto-parietal connectivity serving fluid intelligence

Fluid intelligence (Gf) involves logical reasoning and novel problem-solving abilities. Often, abstract reasoning tasks like Raven's progressive matrices are used to assess Gf. Prior work has shown an age-related decline in fluid intelligence capabilities, and although many studies have sought to identify the underlying mechanisms, our understanding of the critical brain regions and dynamics remains largely incomplete. In this study, we utilized magnetoencephalography (MEG) to investigate 78 individuals, ages 20-65 years, as they completed an abstract reasoning task. MEG data was co-registered with structural MRI data, transformed into the time-frequency domain, and the resulting neural oscillations were imaged using a beamformer. We found worsening behavioral performance with age, including prolonged reaction times and reduced accuracy. MEG analyses indicated robust oscillations in the theta, alpha/beta, and gamma range during the task. Whole brain correlation analyses with age revealed relationships in the theta and alpha/beta frequency bands, such that theta oscillations became stronger with increasing age in a right prefrontal region and alpha/beta oscillations became stronger with increasing age in parietal and right motor cortices. Follow-up connectivity analyses revealed increasing parieto-frontal connectivity with increasing age in the alpha/beta frequency range. Importantly, our findings are consistent with the parieto-frontal integration theory of intelligence (P-FIT). These results further suggest that as people age, there may be alterations in neural responses that are spectrally specific, such that older people exhibit stronger alpha/beta oscillations across the parieto-frontal network during abstract reasoning tasks.

Age-related alterations in the oscillatory dynamics serving verbal working memory processing

Working memory (WM) is a foundational cognitive function involving the temporary storage of information. Unfortunately, WM is also one of the most sensitive cognitive functions to the detrimental effects of aging. Expanding the field's understanding of age-related WM changes is critical to advancing the development of strategies to mitigate age-related WM declines. In the current study, we investigated the neural mechanisms serving WM function in seventy-eight healthy aging adults (range: 20.2-65.2 years) using magnetoencephalography (MEG) and a Sternberg WM task with letter stimuli. Neural activity during the different phases of the WM task (i.e., encoding, maintenance, and retrieval) were imaged using a time-frequency resolved beamformer and whole-brain statistics were performed. We found stronger increases in theta activity and stronger decreases in alpha and beta activity (i.e., more negative relative to baseline) as a function of healthy aging. Specifically, age-related increases in theta activity were detected during the encoding period in the primary visual and left prefrontal cortices. Additionally, alpha and beta oscillations were stronger (i.e., more negative) during both encoding and maintenance in the left prefrontal cortex in older individuals. Finally, alpha and beta oscillations during the retrieval phase were stronger (i.e., more negative) in older participants within the prefrontal, parietal, and temporal cortices. Together, these results indicate that healthy aging strongly modulates the neural oscillatory dynamics serving WM function.

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 (  ∼  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.

Epigenetic aging is associated with aberrant neural oscillatory dynamics serving visuospatial processing in people with HIV

BACKGROUND

Despite effective antiretroviral therapy, cognitive impairment and other aging-related comorbidities are more prevalent in people with HIV (PWH) than in the general population. Previous research examining DNA methylation has shown PWH exhibit accelerated biological aging. However, it is unclear how accelerated biological aging may affect neural oscillatory activity in virally suppressed PWH, and more broadly how such aberrant neural activity may impact neuropsychological performance.

METHODS

In the present study, participants (n = 134) between the ages of 23 - 72 years underwent a neuropsychological assessment, a blood draw to determine biological age via DNA methylation, and a visuospatial processing task during magnetoencephalography (MEG). Our analyses focused on the relationship between biological age and oscillatory theta (4-8 Hz) and alpha (10 - 16 Hz) activity among PWH (n=65) and seronegative controls (n = 69).

RESULTS

PWH had significantly elevated biological age when controlling for chronological age relative to controls. Biological age was differentially associated with theta oscillations in the left posterior cingulate cortex (PCC) and with alpha oscillations in the right medial prefrontal cortex (mPFC) among PWH and seronegative controls. Stronger alpha oscillations in the mPFC were associated with lower CD4 nadir and lower current CD4 counts, suggesting such responses were compensatory. Participants who were on combination antiretroviral therapy for longer had weaker theta oscillations in the PCC.

CONCLUSIONS

These findings support the concept of interactions between biological aging and HIV status on the neural oscillatory dynamics serving visuospatial processing. Future work should elucidate the long-term trajectory and impact of accelerated aging on neural oscillatory dynamics in PWH.

Regular cannabis use modulates the impact of HIV on the neural dynamics serving cognitive control

BACKGROUND

Cannabis use and HIV are independently associated with decrements in cognitive control. However, the combined effects of HIV and regular cannabis use on the brain circuitry serving higher-order cognition are unclear.

AIMS

Investigate the interaction between cannabis and HIV on neural interference effects during the flanker task and spontaneous activity in regions underlying higher-order cognition.

METHODS

The sample consisted of 100 participants, including people with HIV (PWH) who use cannabis, PWH who do not use cannabis, uninfected cannabis users, and uninfected nonusers. Participants underwent an interview regarding their substance use history and completed the Eriksen flanker task during magnetoencephalography (MEG). MEG data were imaged in the time-frequency domain and oscillatory maps depicting the neural flanker interference effect were probed for group differences. Voxel time series were then assessed for group-level differences in spontaneous activity.

RESULTS

Group differences in behavioral performance were identified along with group differences in theta and alpha neural interference responses in higher-order regions across the cortex, with nonusers with HIV generally exhibiting the most aberrant responses. Likewise, time series analyses indicated that nonusers with HIV also had significantly elevated spontaneous alpha activity in the left inferior frontal and dorsolateral prefrontal cortices (dlPFC). Finally, we found that spontaneous and oscillatory alpha activity were significantly coupled in the inferior frontal cortex and dlPFC among cannabis users, but not nonusers.

CONCLUSIONS

Regular cannabis use appears to suppress the impact of HIV on spontaneous and oscillatory alpha deficits in the left inferior frontal cortex and dlPFC.

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.

A Low-Complexity and High-Resolution Beamformer for Portable Medical Ultrasound Imaging

So far, researchers have proposed various methods to improve the quality of medical ultrasound imaging. However, in portable medical ultrasound imaging systems, features, such as low cost and low power consumption for battery longevity, are very important. Hence, most of the proposed algorithms have not been proper substitutes for the delay and sum (DAS) algorithm in portable clinical applications due to their high computational complexity and cost. In this article, a new algorithm is presented concentrating on reducing the computational complexity based on a technique that separates the signal from the correlated interferences to overcome the negative characteristics, particularly for portable applications such as high price, high power consumption, and off-axis clutters in the azimuth direction. Also, the proposed algorithm yields a higher contrast compared to that of the DAS algorithm while achieving a similar computation complexity order of O ( n ) similar to the DAS algorithm. Furthermore, the performed simulations confirm that the proposed method is able to achieve a better resolution almost twice as that of the filtered delay multiply and sum (F-DMAS) algorithm with the same sidelobe level.

Recording EEG in Cochlear Implant Users: Guidelines for Experimental Design and Data Analysis for Optimizing Signal Quality and Minimizing Artifacts

Cochlear implants (CI) are neural prostheses that can restore hearing in individuals with severe to profound hearing loss. Although CIs significantly improve quality of life, clinical outcomes are still highly variable. An important part of this variability is explained by the brain reorganization following cochlear implantation. Therefore, clinicians and researchers are seeking objective measurements to investigate post-implantation brain plasticity. Electroencephalography (EEG) is a promising technique because it is objective, non-invasive, and implant-compatible, but is nonetheless susceptible to massive artifacts generated by the prosthesis's electrical activity. CI artifacts can blur and distort brain responses; thus, it is crucial to develop reliable techniques to remove them from EEG recordings. Despite numerous artifact removal techniques used in previous studies, there is a paucity of documentation and consensus on the optimal EEG procedures to reduce these artifacts. Herein, and through a comprehensive review process, we provide a guideline for designing an EEG-CI experiment minimizing the effect of the artifact. We provide some technical guidance for recording an accurate neural response from CI users and discuss the current challenges in detecting and removing CI-induced artifacts from a recorded signal. The aim of this paper is also to provide recommendations to better appraise and report EEG-CI findings.

Testing covariance models for MEG source reconstruction of hippocampal activity

Beamforming is one of the most commonly used source reconstruction methods for magneto- and electroencephalography (M/EEG). One underlying assumption, however, is that distant sources are uncorrelated and here we tested whether this is an appropriate model for the human hippocampal data. We revised the Empirical Bayesian Beamfomer (EBB) to accommodate specific a-priori correlated source models. We showed in simulation that we could use model evidence (as approximated by Free Energy) to distinguish between different correlated and uncorrelated source scenarios. Using group MEG data in which the participants performed a hippocampal-dependent task, we explored the possibility that the hippocampus or the cortex or both were correlated in their activity across hemispheres. We found that incorporating a correlated hippocampal source model significantly improved model evidence. Our findings help to explain why, up until now, the majority of MEG-reported hippocampal activity (typically making use of beamformers) has been estimated as unilateral.

Cannabis use impacts pre-stimulus neural activity in the visual cortices of people with HIV

People with HIV (PWH) use cannabis at a higher rate than the general population, but the influence on neural activity is not well characterized. Cannabis use among PWH may have a beneficial effect, as neuroinflammation is known to be a critical problem in PWH and cannabis use has been associated with a reduction in proinflammatory markers. Thus, it is important to understand the net impact of cannabis use on brain and cognitive function in PWH. In this study, we collected magnetoencephalographic (MEG) brain imaging data on 81 participants split across four demographically matched groups (i.e., PWH using cannabis, controls using cannabis, non‐using PWH, and non‐using controls). Participants completed a visuospatial processing task during MEG. Time–frequency resolved voxel time series were extracted to identify the dynamics of oscillatory and pre‐stimulus baseline neural activity. Our results indicated strong theta (4–8 Hz), alpha (10–16 Hz), and gamma (62–72 Hz) visual oscillations in parietal–occipital brain regions across all participants. PWH exhibited significant behavioral deficits in visuospatial processing, as well as reduced theta oscillations and elevated pre‐stimulus gamma activity in visual cortices, all of which replicate prior work. Strikingly, chronic cannabis use was associated with a significant reduction in pre‐stimulus gamma activity in the visual cortices, such that PWH no longer statistically differed from controls. These results provide initial evidence that cannabis use may normalize some neural aberrations in PWH. This study fills an important gap in understanding the impact of cannabis use on brain and cognitive function in PWH.

MEG Source Localization via Deep Learning

We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals. The proposed deep model architectures are tuned to single and multiple time point MEG data, and can estimate varying numbers of dipole sources. Results from simulated MEG data on the cortical surface of a real human subject demonstrated improvements against the popular RAP-MUSIC localization algorithm in specific scenarios with varying SNR levels, inter-source correlation values, and number of sources. Importantly, the deep learning models had robust performance to forward model errors resulting from head translation and rotation and a significant reduction in computation time, to a fraction of 1 ms, paving the way to real-time MEG source localization.

The identification of predominant auditory steady-state response brain sources in electroencephalography using denoising source separation

Different approaches have been used to extract auditory steady-state responses (ASSRs) from electroencephalography (EEG) recordings, including region-related electrode configurations (electrode level) and the manual placement of equivalent current dipoles (source level). Inherent limitations of these approaches are the assumption of the anatomical origin and the omission of activity generated by secondary sources. Data-driven methods such as independent component analysis (ICA) seem to avoid these limitations but only to face new others such as the presence of ASSRs with similar properties in different components and the manual selection protocol to select and classify the most relevant components carrying ASSRs. We propose the novel approach of applying a spatial filter to these components in order to extract the most relevant information. We aimed to develop a method based on the reproducibility across trials that performs reliably in low-signal-to-noise ratio (SNR) scenarios using denoising source separation (DSS). DSS combined with ICA successfully reduced the number of components and extracted the most relevant ASSR at 4, 10 and 20 Hz stimulation in group and individual level studies of EEG adolescent data. The anatomical brain location for these low stimulation frequencies showed results in cortical areas with relatively small dispersion. However, for 40 and 80 Hz, results with regard to the number of components and the anatomical origin were less clear. At all stimulation frequencies the outcome measures were consistent with literature, and the partial rejection of inter-subject variability led to more accurate results and higher SNRs. These findings are promising for future applications in group comparison involving pathologies.

Modified covariance beamformer for solving MEG inverse problem in the environment with correlated sources

Magnetoencephalography (MEG) is a neuroimaging method ideally suited for non-invasive studies of brain dynamics. MEG's spatial resolution critically depends on the approach used to solve the ill-posed inverse problem in order to transform sensor signals into cortical activation maps. Over recent years non-globally optimized solutions based on the use of adaptive beamformers (BF) gained popularity. When operating in the environment with a small number of uncorrelated sources the BFs perform optimally and yield high spatial resolution. However, the BFs are known to fail when dealing with correlated sources acting like poorly tuned spatial filters with low signal-to-noise ratio (SNR) of the output timeseries and often meaningless cortical maps of power distribution. This fact poses a serious limitation on the broader use of this promising technique especially since fundamental mechanisms of brain functioning, its inherent symmetry and task-based experimental paradigms result into a great deal of correlation in the activity of cortical sources. To cope with this problem, we developed a novel data covariance modification approach that allows for building beamformers that maintain high spatial resolution when operating in the environments with correlated sources. At the core of our method is a projection operation applied to the vectorized sensor-space covariance matrix. This projection does not remove the activity of the correlated sources from the sensor-space covariance matrix but rather selectively handles their contributions to the covariance matrix and creates a sufficiently accurate approximation of an ideal data covariance that could hypothetically be observed should these sources be uncorrelated. Since the projection operation is reciprocal to the PSIICOS method developed by us earlier (Ossadtchi et al., 2018) we refer to the family of algorithms presented here as ReciPSIICOS. We assess the performance of the novel approach using realistically simulated MEG data and show its superior performance in comparison to the classical BF approaches and well established MNE as a method immune to source synchrony by design. We have also applied our approach to the MEG datasets from the two experiments involving two different auditory tasks. The analysis of experimental MEG datasets showed that beamformers from ReciPSIICOS family, but not the classical BF, discovered the expected bilateral focal sources in the primary auditory cortex and detected motor cortex activity associated with the audio-motor task. In most cases MNE managed well but as expected produced more spatially diffuse source distributions. Notably, ReciPSIICOS beamformers yielded cortical activity estimates with SNR several times higher than that obtained with the classical BF, which may indirectly indicate the severeness of the signal cancellation problem when applying classical beamformers to MEG signals generated by synchronous sources.

Functional Connectivity Hypersynchronization in Relatives of Alzheimer’s Disease Patients: An Early E/I Balance Dysfunction?

Alzheimer’s disease (AD) studies on animal models, and humans showed a tendency of the brain tissue to become hyperexcitable and hypersynchronized, causing neurodegeneration. However, we know little about either the onset of this phenomenon or its early effects on functional brain networks. We studied functional connectivity (FC) on 127 participants (92 middle-age relatives of AD patients and 35 age-matched nonrelatives) using magnetoencephalography. FC was estimated in the alpha band in areas known both for early amyloid accumulation and disrupted FC in MCI converters to AD. We found a frontoparietal network (anterior cingulate cortex, dorsal frontal, and precuneus) where relatives of AD patients showed hypersynchronization in high alpha (not modulated by APOE-ε4 genotype) in comparison to age-matched nonrelatives. These results represent the first evidence of neurophysiological events causing early network disruption in humans, opening a new perspective for intervention on the excitation/inhibition unbalance.

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.

Spatio-temporal Reconstruction of Neural Sources Using Indirect Dominant Mode Rejection

Adaptive minimum variance based beamformers (MVB) have been successfully applied to magnetoencephalogram (MEG) and electroencephalogram (EEG) data to localize brain activities. However, the performance of these beamformers falls down in situations where correlated or interference sources exist. To overcome this problem, we propose indirect dominant mode rejection (iDMR) beamformer application in brain source localization. This method by modifying measurement covariance matrix makes MVB applicable in source localization in the presence of correlated and interference sources. Numerical results on both EEG and MEG data demonstrate that presented approach accurately reconstructs time courses of active sources and localizes those sources with high spatial resolution. In addition, the results of real AEF data show the good performance of iDMR in empirical situations. Hence, iDMR can be reliably used for brain source localization especially when there are correlated and interference sources.

Localizing Event-Related Potentials Using Multi-source Minimum Variance Beamformers: A Validation Study

Adaptive and non-adaptive beamformers have become a prominent neuroimaging tool for localizing neural sources of electroencephalographic (EEG) and magnetoencephalographic (MEG) data. In this study, we investigated single-source and multi-source scalar beamformers with respect to their performances in localizing and reconstructing source activity for simulated and real EEG data. We compared a new multi-source search approach (multi-step iterative approach; MIA) to our previous multi-source search approach (single-step iterative approach; SIA) and a single-source search approach (single-step peak approach; SPA). In order to compare performances across these beamformer approaches, we manipulated various simulated source parameters, such as the amount of signal-to-noise ratio (0.1-0.9), inter-source correlations (0.3-0.9), number of simultaneously active sources (2-8), and source locations. Results showed that localization performance followed the order of MIA > SIA > SPA regardless of the number of sources, source correlations, and single-to-noise ratios. In addition, SIA and MIA were significantly better than SPA at localizing four or more sources. Moreover, MIA was better than SIA and SPA at identifying the true source locations when signal characteristics were at their poorest. Source waveform reconstructions were similar between MIA and SIA but were significantly better than that for SPA. A similar trend was also found when applying these beamformer approaches to a real EEG dataset. Based on our findings, we conclude that multi-source beamformers (MIA and SIA) are an improvement over single-source beamformers for localizing EEG. Importantly, our new search method, MIA, had better localization performance, localization precision, and source waveform reconstruction as compared to SIA or SPA. We therefore recommend its use for improved source localization and waveform reconstruction of event-related potentials.

Cortical organization restored by cochlear implantation in young children with single sided deafness

Early treatment of single sided deafness in children has been recommended to protect from neurodevelopmental preference for the better hearing ear and from social and educational deficits. A fairly homogeneous group of five young children (≤3.6 years of age) with normal right sided hearing who received a cochlear implant to treat deafness in their left ears were studied. Etiology of deafness was largely cytomegalovirus (n = 4); one child had an enlarged vestibular aqueduct. Multi-channel electroencephalography of cortical evoked activity was measured repeatedly over time at: 1) acute (0.5 ± 0.7 weeks); 2) early chronic (1.1 ± 0.2 months); and 3) chronic (5.8 ± 3.4 months) cochlear implant stimulation. Results indicated consistent responses from the normal right ear with marked changes in activity from the implanted left ear. Atypical distribution of peak amplitude activity from the implanted ear at acute stimulation marked abnormal lateralization of activity to the ipsilateral left auditory cortex and recruitment of extra-temporal areas including left frontal cortex. These abnormalities resolved with chronic implant use and contralateral aural preference emerged in both auditory cortices. These findings indicate that early implantation in young children with single sided deafness can rapidly restore bilateral auditory input to the cortex needed to improve binaural hearing.

Delayed access to bilateral input alters cortical organization in children with asymmetric hearing

Bilateral hearing in early development protects auditory cortices from reorganizing to prefer the better ear. Yet, such protection could be disrupted by mismatched bilateral input in children with asymmetric hearing who require electric stimulation of the auditory nerve from a cochlear implant in their deaf ear and amplified acoustic sound from a hearing aid in their better ear (bimodal hearing). Cortical responses to bimodal stimulation were measured by electroencephalography in 34 bimodal users and 16 age-matched peers with normal hearing, and compared with the same measures previously reported for 28 age-matched bilateral implant users. Both auditory cortices increasingly favoured the better ear with delay to implanting the deaf ear; the time course mirrored that occurring with delay to bilateral implantation in unilateral implant users. Preference for the implanted ear tended to occur with ongoing implant use when hearing was poor in the non-implanted ear. Speech perception deteriorated with longer deprivation and poorer access to high-frequencies. Thus, cortical preference develops in children with asymmetric hearing but can be avoided by early provision of balanced bimodal stimulation. Although electric and acoustic stimulation differ, these inputs can work sympathetically when used bilaterally given sufficient hearing in the non-implanted ear.

Simultaneous bilateral cochlear implants: Developmental advances do not yet achieve normal cortical processing

BACKGROUND

Simultaneous bilateral cochlear implantation promotes symmetric development of bilateral auditory pathways but binaural hearing remains abnormal. To evaluate whether bilateral cortical processing remains impaired in such children, cortical activity to unilateral and bilateral stimuli was assessed in a unique cohort of 16 children who received bilateral cochlear implants (CIs) simultaneously at 1.97 ± 0.86 years of age and had ~4 years of CI experience, providing the first opportunity to assess electrically driven cortical development in the absence of reorganized asymmetries from sequential implantation.

METHODS

Cortical activity to unilateral and bilateral stimuli was measured using multichannel electro-encephalography. Cortical processing in children with bilateral CIs was compared with click-elicited activity in 13 normal hearing children matched for time-in-sound. Source activity was localized using the Time Restricted, Artefact and Coherence source Suppression (TRACS) beamformer method.

RESULTS

Consistent with dominant crossed auditory pathways, normal P1 activity (~100 ms) was weaker to ipsilateral stimuli relative to contralateral and bilateral stimuli and both auditory cortices preferentially responded to the contralateral ear. Right hemisphere dominance was evident overall. Children with bilateral CIs maintained the expected right dominance but differences from normal included: (i) minimal changes between ipsilateral, contralateral and bilateral stimuli, (ii) weaker than normal contralateral stimulus preference, (iii) symmetric activity to bilateral stimuli, and (iv) increased occipital lobe recruitment during bilateral relative to unilateral stimulation. Between-group contrasts demonstrated lower than normal activity in the inferior parieto-occipital lobe (suggesting deficits in sensory integration) and greater than normal left frontal lobe activity (suggesting increased attention), even during passive listening.

CONCLUSIONS

Together, findings suggest that early simultaneous bilateral cochlear implantation promotes normal-like auditory symmetry but that abnormalities in cortical processing consequent to deafness and/or electrical stimulation through two independent speech processors persist.

Cortical Representation of Interaural Time Difference Is Impaired by Deafness in Development: Evidence from Children with Early Long-term Access to Sound through Bilateral Cochlear Implants Provided Simultaneously

Accurate use of interaural time differences (ITDs) for spatial hearing may require access to bilateral auditory input during sensitive periods in human development. Providing bilateral cochlear implants (CIs) simultaneously promotes symmetrical development of bilateral auditory pathways but does not support normal ITD sensitivity. Thus, although binaural interactions are established by bilateral CIs in the auditory brainstem, potential deficits in cortical processing of ITDs remain. Cortical ITD processing in children with simultaneous bilateral CIs and normal hearing with similar time-in-sound was explored in the present study. Cortical activity evoked by bilateral stimuli with varying ITDs (0, ±0.4, ±1 ms) was recorded using multichannel electroencephalography. Source analyses indicated dominant activity in the right auditory cortex in both groups but limited ITD processing in children with bilateral CIs. In normal-hearing children, adult-like processing patterns were found underlying the immature P1 (∼100 ms) response peak with reduced activity in the auditory cortex ipsilateral to the leading ITD. Further, the left cortex showed a stronger preference than the right cortex for stimuli leading from the contralateral hemifield. By contrast, children with CIs demonstrated reduced ITD-related changes in both auditory cortices. Decreased parieto-occipital activity, possibly involved in spatial processing, was also revealed in children with CIs. Thus, simultaneous bilateral implantation in young children maintains right cortical dominance during binaural processing but does not fully overcome effects of deafness using present CI devices. Protection of bilateral pathways through simultaneous implantation might be capitalized for ITD processing with signal processing advances, which more consistently represent binaural timing cues.SIGNIFICANCE STATEMENT Multichannel electroencephalography demonstrated impairment of binaural processing in children who are deaf despite early access to bilateral auditory input by first finding that foundations for binaural hearing are normally established during early stages of cortical development. Although 4- to 7-year-old children with normal hearing had immature cortical responses, adult patterns in cortical coding of binaural timing cues were measured. Second, children receiving two cochlear implants in the same surgery maintained normal-like input from both ears, but this did not support significant effects of binaural timing cues in either auditory cortex. Deficits in parieto-occiptal areas further suggested impairment in spatial processing. Results indicate that cochlear implants working independently in each ear do not fully overcome deafness-related binaural processing deficits, even after long-term experience.

Source analysis of auditory steady-state responses in acoustic and electric hearing

Speech is a complex signal containing a broad variety of acoustic information. For accurate speech reception, the listener must perceive modulations over a range of envelope frequencies. Perception of these modulations is particularly important for cochlear implant (CI) users, as all commercial devices use envelope coding strategies. Prolonged deafness affects the auditory pathway. However, little is known of how cochlear implantation affects the neural processing of modulated stimuli. This study investigates and contrasts the neural processing of envelope rate modulated signals in acoustic and CI listeners. Auditory steady-state responses (ASSRs) are used to study the neural processing of amplitude modulated (AM) signals. A beamforming technique is applied to determine the increase in neural activity relative to a control condition, with particular attention paid to defining the accuracy and precision of this technique relative to other tomographies. In a cohort of 44 acoustic listeners, the location, activity and hemispheric lateralisation of ASSRs is characterised while systematically varying the modulation rate (4, 10, 20, 40 and 80Hz) and stimulation ear (right, left and bilateral). We demonstrate a complex pattern of laterality depending on both modulation rate and stimulation ear that is consistent with, and extends, existing literature. We present a novel extension to the beamforming method which facilitates source analysis of electrically evoked auditory steady-state responses (EASSRs). In a cohort of 5 right implanted unilateral CI users, the neural activity is determined for the 40Hz rate and compared to the acoustic cohort. Results indicate that CI users activate typical thalamic locations for 40Hz stimuli. However, complementary to studies of transient stimuli, the CI population has atypical hemispheric laterality, preferentially activating the contralateral hemisphere.

A multi-resolution approach to localize neural sources of P300 event-related brain potential

BACKGROUND AND OBJECTIVE

P300 is probably the most well-known component of event-related brain potentials (ERPs). Using an oddball paradigm, a P300 component can be identified, that is, elicited by the target stimuli recognition. Since P300 is associated with attention and memory operations of the brain, investigation of this component can improve our understanding of these mechanisms. The present study is aimed at identifying the P300 generators in 30 healthy subjects aged 18-30 years using time-reduction region-suppression linearly constrained minimum variance (TR-LCMV) beamformer.

METHODS

In our study, TR-LCMV beamformer with multi-resolution approach is proposed, coarse-resolution space to find the approximated coherent source locations, fine-resolution space to estimate covariance matrix for dimension reduction of determined regions, and normal-resolution space to localize the P300 generators in the brain.

RESULTS

Our results over simulated and real data showed that this approach is a suitable tool to the analysis of ERP fields with localizing superior and inferior frontal lobe, middle temporal gyrus, parietal lobe, and cingulate gyrus as the most prominent sources of P300. The result of P300 localization was finally compared with the other localization methods and it is demonstrated that enhanced performance is achieved.

CONCLUSIONS

Our results showed that the P300 originates from a widespread neuronal network in the brain and not from a specific region. Our finding over simulated and real data demonstrated the ability of the TR-LCMV algorithm for P300 source localization.

Localising the auditory N1m with event-related beamformers: localisation accuracy following bilateral and unilateral stimulation

The auditory evoked N1m-P2m response complex presents a challenging case for MEG source-modelling, because symmetrical, phase-locked activity occurs in the hemispheres both contralateral and ipsilateral to stimulation. Beamformer methods, in particular, can be susceptible to localisation bias and spurious sources under these conditions. This study explored the accuracy and efficiency of event-related beamformer source models for auditory MEG data under typical experimental conditions: monaural and diotic stimulation; and whole-head beamformer analysis compared to a half-head analysis using only sensors from the hemisphere contralateral to stimulation. Event-related beamformer localisations were also compared with more traditional single-dipole models. At the group level, the event-related beamformer performed equally well as the single-dipole models in terms of accuracy for both the N1m and the P2m, and in terms of efficiency (number of successful source models) for the N1m. The results yielded by the half-head analysis did not differ significantly from those produced by the traditional whole-head analysis. Any localisation bias caused by the presence of correlated sources is minimal in the context of the inter-individual variability in source localisations. In conclusion, event-related beamformers provide a useful alternative to equivalent-current dipole models in localisation of auditory evoked responses.

How reliable are MEG resting-state connectivity metrics?

MEG offers dynamic and spectral resolution for resting-state connectivity which is unavailable in fMRI. However, there are a wide range of available network estimation methods for MEG, and little in the way of existing guidance on which ones to employ. In this technical note, we investigate the extent to which many popular measures of stationary connectivity are suitable for use in resting-state MEG, localising magnetic sources with a scalar beamformer. We use as empirical criteria that network measures for individual subjects should be repeatable, and that group-level connectivity estimation shows good reproducibility. Using publically-available data from the Human Connectome Project, we test the reliability of 12 network estimation techniques against these criteria. We find that the impact of magnetic field spread or spatial leakage artefact is profound, creates a major confound for many connectivity measures, and can artificially inflate measures of consistency. Among those robust to this effect, we find poor test-retest reliability in phase- or coherence-based metrics such as the phase lag index or the imaginary part of coherency. The most consistent methods for stationary connectivity estimation over all of our tests are simple amplitude envelope correlation and partial correlation measures.

Evidence for a Caregiving Instinct: Rapid Differentiation of Infant from Adult Vocalizations Using Magnetoencephalography

Crying is the most salient vocal signal of distress. The cries of a newborn infant alert adult listeners and often elicit caregiving behavior. For the parent, rapid responding to an infant in distress is an adaptive behavior, functioning to ensure offspring survival. The ability to react rapidly requires quick recognition and evaluation of stimuli followed by a co-ordinated motor response. Previous neuroimaging research has demonstrated early specialized activity in response to infant faces. Using magnetoencephalography, we found similarly early (100-200 ms) differences in neural responses to infant and adult cry vocalizations in auditory, emotional, and motor cortical brain regions. We propose that this early differential activity may help to rapidly identify infant cries and engage affective and motor neural circuitry to promote adaptive behavioral responding, before conscious awareness. These differences were observed in adults who were not parents, perhaps indicative of a universal brain-based "caregiving instinct."

Early unilateral cochlear implantation promotes mature cortical asymmetries in adolescents who are deaf

Unilateral cochlear implant (CI) stimulation establishes hearing to children who are deaf but compromises bilateral auditory development if a second implant is not provided within ∼ 1.5 years. In this study we asked: 1) What are the cortical consequences of missing this early sensitive period once children reach adolescence? 2) What are the effects of unilateral deprivation on the pathways from the opposite ear? Cortical responses were recorded from 64-cephalic electrodes within the first week of bilateral CI activation in 34 adolescents who had over 10 years of unilateral right CI experience and in 16 normal hearing peers. Cortical activation underlying the evoked peaks was localized to areas of the brain using beamformer imaging. The first CI evoked activity which was more strongly lateralized to the contralateral left hemisphere than normal, with abnormal recruitment of the left prefrontal cortex (involved in cognition/attention), left temporo-parietal-occipital junction (multi-modal integration), and right precuneus (visual processing) region. CI stimulation in the opposite deprived ear evoked atypical cortical responses with abnormally large and widespread dipole activity across the cortex. Thus, using a unilateral CI to hear beyond the period of cortical maturation causes lasting asymmetries in the auditory system, requires recruitment of additional cortical areas to support hearing, and does little to protect the unstimulated pathways from effects of auditory deprivation. The persistence of this reorganization into maturity could signal a closing of a sensitive period for promoting auditory development on the deprived side.

Measuring electrophysiological connectivity by power envelope correlation: a technical review on MEG methods

The human brain can be divided into multiple areas, each responsible for different aspects of behaviour. Healthy brain function relies upon efficient connectivity between these areas and, in recent years, neuroimaging has been revolutionised by an ability to estimate this connectivity. In this paper we discuss measurement of network connectivity using magnetoencephalography (MEG), a technique capable of imaging electrophysiological brain activity with good (~5 mm) spatial resolution and excellent (~1 ms) temporal resolution. The rich information content of MEG facilitates many disparate measures of connectivity between spatially separate regions and in this paper we discuss a single metric known as power envelope correlation. We review in detail the methodology required to measure power envelope correlation including (i) projection of MEG data into source space, (ii) removing confounds introduced by the MEG inverse problem and (iii) estimation of connectivity itself. In this way, we aim to provide researchers with a description of the key steps required to assess envelope based functional networks, which are thought to represent an intrinsic mode of coupling in the human brain. We highlight the principal findings of the techniques discussed, and furthermore, we show evidence that this method can probe how the brain forms and dissolves multiple transient networks on a rapid timescale in order to support current processing demand. Overall, power envelope correlation offers a unique and verifiable means to gain novel insights into network coordination and is proving to be of significant value in elucidating the neural dynamics of the human connectome in health and disease.

A Beamformer-Particle Filter Framework for Localization of Correlated EEG Sources

Electroencephalography (EEG)-based brain computer interface (BCI) is the most studied noninvasive interface to build a direct communication pathway between the brain and an external device. However, correlated noises in EEG measurements still constitute a significant challenge. Alternatively, building BCIs based on filtered brain activity source signals instead of using their surface projections, obtained from the noisy EEG signals, is a promising and not well-explored direction. In this context, finding the locations and waveforms of inner brain sources represents a crucial task for advancing source-based noninvasive BCI technologies. In this paper, we propose a novel multicore beamformer particle filter (multicore BPF) to estimate the EEG brain source spatial locations and their corresponding waveforms. In contrast to conventional (single-core) beamforming spatial filters, the developed multicore BPF considers explicitly temporal correlation among the estimated brain sources by suppressing activation from regions with interfering coherent sources. The hybrid multicore BPF brings together the advantages of both deterministic and Bayesian inverse problem algorithms in order to improve the estimation accuracy. It solves the brain activity localization problem without prior information about approximate areas of source locations. Moreover, the multicore BPF reduces the dimensionality of the problem to half compared with the PF solution, thus alleviating the curse of dimensionality problem. The results, based on generated and real EEG data, show that the proposed framework recovers correctly the dominant sources of brain activity.

Influence of the head model on EEG and MEG source connectivity analyses

The results of brain connectivity analysis using reconstructed source time courses derived from EEG and MEG data depend on a number of algorithmic choices. While previous studies have investigated the influence of the choice of source estimation method or connectivity measure, the effects of the head modeling errors or simplifications have not been studied sufficiently. In the present simulation study, we investigated the influence of particular properties of the head model on the reconstructed source time courses as well as on source connectivity analysis in EEG and MEG. Therefore, we constructed a realistic head model and applied the finite element method to solve the EEG and MEG forward problems. We considered the distinction between white and gray matter, the distinction between compact and spongy bone, the inclusion of a cerebrospinal fluid (CSF) compartment, and the reduction to a simple 3-layer model comprising only the skin, skull, and brain. Source time courses were reconstructed using a beamforming approach and the source connectivity was estimated by the imaginary coherence (ICoh) and the generalized partial directed coherence (GPDC). Our results show that in both EEG and MEG, neglecting the white and gray matter distinction or the CSF causes considerable errors in reconstructed source time courses and connectivity analysis, while the distinction between spongy and compact bone is just of minor relevance, provided that an adequate skull conductivity value is used. Large inverse and connectivity errors are found in the same regions that show large topography errors in the forward solution. Moreover, we demonstrate that the very conservative ICoh is relatively safe from the crosstalk effects caused by imperfect head models, as opposed to the GPDC.

Volumetric imaging of brain activity with spatial-frequency decoding of neuromagnetic signals

BACKGROUND

The brain generates signals in a wide frequency range (∼2840 Hz). Existing magnetoencephalography (MEG) methods typically detect brain activity in a median-frequency range (1-70 Hz). The objective of the present study was to develop a new method to utilize the frequency signatures for source imaging.

NEW METHOD

Morlet wavelet transform and two-step beamforming were integrated into a systematic approach to estimate magnetic sources in time-frequency domains. A grid-frequency kernel (GFK) was developed to decode the correlation between each time-frequency representation and grid voxel. Brain activity was reconstructed by accumulating spatial- and frequency-locked signals in the full spectral data for all grid voxels. To test the new method, MEG data were recorded from 20 healthy subjects and 3 patients with verified epileptic foci.

RESULTS

The experimental results showed that the new method could accurately localize brain activation in auditory cortices. The epileptic foci localized with the new method were spatially concordant with invasive recordings.

COMPARISON WITH EXISTING METHODS

Compared with well-known existing methods, the new method is objective because it scans the entire brain without making any assumption about the number of sources. The novel feature of the new method is its ability to localize high-frequency sources.

CONCLUSIONS

The new method could accurately localize both low- and high-frequency brain activities. The detection of high-frequency MEG signals can open a new avenue in the study of the human brain function as well as a variety of brain disorders.