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• Persistently high inflammatory levels are known to lead to major health problems. • CRP and TNF-α in particular have been linked to cognitive impairment and decline. • Neural oscillations serving abstract reasoning were linked to CRP and TNF-α. • Elevations in CRP and TNF-α scaled with weaker oscillations during task performance. • CRP and TNF-α levels may be pre-clinical markers of future cognitive decline. Inflammatory processes help protect the body from potential threats such as bacterial or viral invasions. However, when such inflammatory processes become chronically engaged, synaptic impairments and neuronal cell death may occur. In particular, persistently high levels of C-reactive protein (CRP) and tumor necrosis factor-alpha (TNF-α) have been linked to deficits in cognition and several psychiatric disorders. Higher-order cognitive processes such as fluid intelligence (G f) are thought to be particularly vulnerable to persistent inflammation. Herein, we investigated the relationship between elevated CRP and TNF-α and the neural oscillatory dynamics serving G f. Seventy adults between the ages of 20–66 years (Mean = 45.17 years, SD = 16.29, 21.4% female) completed an abstract reasoning task that probes G f during magnetoencephalography (MEG) and provided a blood sample for inflammatory marker analysis. MEG data were imaged in the time–frequency domain, and whole-brain regressions were conducted using each individual's plasma CRP and TNF-α concentrations per oscillatory response, controlling for age, BMI, and education. CRP and TNF-α levels were significantly associated with region-specific neural oscillatory responses. In particular, elevated CRP concentrations were associated with altered gamma activity in the right inferior frontal gyrus and right cerebellum. In contrast, elevated TNF-α levels scaled with alpha/beta oscillations in the left anterior cingulate and left middle temporal, and gamma activity in the left intraparietal sulcus. Elevated inflammatory markers such as CRP and TNF-α were associated with aberrant neural oscillations in regions important for G f. Linking inflammatory markers with regional neural oscillations may hold promise in identifying mechanisms of cognitive and psychiatric disorders. [ABSTRACT FROM AUTHOR]

Despite virologic suppression, people living with HIV (PLWH) remain at risk for developing cognitive impairment, with aberrations in motor control being a predominant symptom leading to functional dependencies in later life. While the neuroanatomical bases of motor dysfunction have recently been illuminated, the underlying molecular processes remain poorly understood. Herein, we evaluate the predictive capacity of the mitochondrial redox environment on sensorimotor brain-behavior dynamics in 40 virally-suppressed PLWH and 40 demographically-matched controls using structural equation modeling. We used state-of-the-art approaches, including Seahorse Analyzer of mitochondrial function, electron paramagnetic resonance spectroscopy to measure superoxide levels, antioxidant activity assays and dynamic magnetoencephalographic imaging to quantify sensorimotor oscillatory dynamics. We observed differential modulation of sensorimotor brain-behavior relationships by superoxide and hydrogen peroxide-sensitive features of the redox environment in PLWH, while only superoxide-sensitive features were related to optimal oscillatory response profiles and better motor performance in controls. Moreover, these divergent pathways may be attributable to immediate, separable mechanisms of action within the redox environment seen in PLWH, as evidenced by mediation analyses. These findings suggest that mitochondrial redox parameters are important modulators of healthy and pathological oscillations in motor systems and behavior, serving as potential targets for remedying HIV-related cognitive-motor dysfunction in the future. [ABSTRACT FROM AUTHOR]

Optically pumped magnetometer magnetoencephalography (OPM-MEG) holds significant promise for clinical functional brain imaging due to its superior spatiotemporal resolution. However, effectively suppressing metallic artifacts, particularly from devices such as orthodontic braces and vagal nerve stimulators remains a major challenge, hindering the wider clinical application of wearable OPM-MEG devices. A comprehensive analysis of metal artifact characteristics from time, frequency, and time–frequency perspectives was conducted for the first time using an OPM-MEG device in clinical medicine. This study focused on patients with metal orthodontics, examining the modulation of metal artifacts by breath and head movement, the incomplete regular sub-Gaussian distribution, and the high absolute power ratio in the 0.5–8 Hz band. The existing metal artifact suppression algorithms applied to SQUID-MEG, such as fast independent component analysis (FastICA), information maximization (Infomax), and algorithms for multiple unknown signal extraction (AMUSE), exhibit limited efficacy. Consequently, this study introduced the second-order blind identification (SOBI) algorithm, which utilized multiple time delays for the component separation of OPM-MEG measurement signals. We modified the time delays of the SOBI method to improve its efficacy in separating artifact components, particularly those in the ultralow frequency range. This approach employs the frequency-domain absolute power ratio, root mean square (RMS) value, and mutual information methods to automate the artifact component screening process. The effectiveness of this method was validated through simulation experiments involving four subjects in both resting and evoked experiments. In addition, the proposed method was also validated by the actual OPM-MEG evoked experiments of three subjects. Comparative analyses were conducted against the FastICA, Infomax, and AMUSE algorithms. Evaluation metrics included normalized mean square error, normalized delta band power error, RMS error, and signal-to-noise ratio, demonstrating that the proposed method provides optimal suppression of metal artifacts. This advancement holds promise for enhancing data quality and expanding the clinical applications of OPM-MEG. [Display omitted] • The atomic magnetometers are getting more and more attention. • The metallic artifacts hinder the clinical application of wearable OPM-MEG. • The proposed method in this article can effectively suppress the dental metal artifact. [ABSTRACT FROM AUTHOR]

Our findings suggest that higher-order cognitive processes involved in emotion conflict regulation are impaired in epilepsy and should be targeted in treatments for epilepsy and its psychiatric comorbidities. [Display omitted] • Magnetoencephalogram reveals emotion regulation differences in adolescent epilepsy. • Lower activity in emotion and conflict related regions post-response in epilepsy. • Behavioral and brain responses differed between generalized and focal epilepsy. • Brain responses predicted depression and anxiety differently by epilepsy type. • Emotion regulation may be a treatment target for epilepsy and its comorbidities. People with epilepsy often suffer from comorbid psychiatric disorders, which negatively affects their quality of life. Emotion regulation is an important cognitive process that is impaired in individuals with psychiatric disorders, such as depression. Adults with epilepsy also show difficulties in emotion regulation, particularly during later-stage, higher-order cognitive processing. Yet, the spatiotemporal and frequency correlates of these functional brain deficits in epilepsy remain unknown, as do the nature of these deficits in adolescent epilepsy. Here, we aim to elucidate the spatiotemporal profile of emotional conflict processing in adolescents with epilepsy, relative to controls, using magnetoencephalography (MEG) and relate these findings to anxiety and depression symptom severity assessed with self-report scales. We hypothesized to see blunted brain activity during emotional conflict in adolescents with epilepsy, relative to controls, in the posterior parietal, prefrontal and cingulate cortices due to their role in explicit and implicit regulation around participant response (500–1000 ms). We analyzed MEG recordings from 53 adolescents (28 epilepsy [14focal,14generalized], 25 controls) during an emotional conflict task. We showed that while controls exhibited behavioral interference to emotional conflict, adolescents with epilepsy failed to exhibit this normative response time pattern. Adolescents with epilepsy showed blunted brain responses to emotional conflict in brain regions related to error evaluation and learning around the average response time (500–700 ms), and in regions involved in decision making during post-response monitoring (800–1000 ms). Interestingly, behavioral patterns and psychiatric symptom severity varied between epilepsy subgroups, wherein those with focal epilepsy showed preserved response time interference. Thus, brain responses were regressed with depression and anxiety levels for each epilepsy subgroup separately. Analyses revealed that under activation in error evaluation regions (500–600 ms) predicted anxiety and depression in focal epilepsy, while regions related to learning (600–700 ms) predicted anxiety in generalized epilepsy, suggesting differential mechanisms of dysfunction in these subgroups. Despite similar rates of anxiety and depression across the groups, adolescents with epilepsy still exhibited deficits in emotional conflict processing in brain and behavioral responses. This suggests that these deficits may exist independently from psychopathology and may stem from underlying dysfunctions that predispose these individuals to develop both disorders. Findings such as these may provide potential targets for future research and therapies. [ABSTRACT FROM AUTHOR]

The default-mode network (DMN) and its principal core hubs in the posterior midline cortices (PMC), i.e., the precuneus and the posterior cingulate cortex, play a critical role in the human brain structural and functional architecture. Because of their centrality, they are affected by a wide spectrum of brain disorders, e.g., Alzheimer's disease. Non-invasive electrophysiological techniques such as magnetoencephalography (MEG) are crucial to the investigation of the neurophysiology of the DMN and its alteration by brain disorders. However, MEG studies relying on band-limited power envelope correlation diverge in their ability to identify the PMC as a part of the DMN in healthy subjects at rest. Since these works were based on different MEG recording systems and different source reconstruction pipelines, we compared DMN functional connectivity estimated with two distinct MEG systems (Elekta, now MEGIN, and CTF) and two widely used reconstruction algorithms (Minimum Norm Estimation and linearly constrained minimum variance Beamformer). Our results identified the reconstruction method as the critical factor influencing PMC functional connectivity, which was significantly dampened by the Beamformer. On this basis, we recommend that future electrophysiological studies on the DMN should rely on Minimum Norm Estimation (or close variants) rather than on the classical Beamformer. Crucially, based on analytic knowledge about these two reconstruction algorithms, we demonstrated with simulations that this empirical observation could be explained by the existence of a spontaneous linear, approximately zero-lag synchronization structure between areas of the DMN or among multiple sources within the PMC. This finding highlights a novel property of the neural dynamics and functional architecture of a core human brain network at rest. • We investigate sources of inconsistencies in MEG DMN functional connectivity. • MNE captures functional connectivity at the posterior midline cortices. • LCMV Beamformer suppresses functional connectivity at the posterior midline cortices. • We recommend using MNE instead of LCMV Beamformer for MEG studies of the DMN. • Simulations suggest a spontaneous linear correlation structure within the DMN. [ABSTRACT FROM AUTHOR]

The auditory cortex is sensitive to many forms of acoustic regularity, resulting in suppressed neural activity for expected auditory events. It is unclear whether this activity reduction for expected events is the result of suppression of neurons that are tuned to the expected stimulus (i.e., dampening), or alternatively suppression of neurons that are tuned away from the expected stimulus (i.e., sharpening). In the present study, we adjudicated between these models by characterizing the effect of expectation on the ability to classify the identity of auditory stimuli from auditory neural activity patterns, using magnetoencephalography (MEG) in healthy human observers. Participants listened to pure tone pairs, in which the identity of the second tone was either expected or unexpected. The task of the participants was to detect a target tone, which deviated strongly from both the expected and unexpected tones. We found a strong suppression of the overall neural response in the expected condition compared to the unexpected condition. Linear classifiers showed a reduced ability to decode stimulus identity from event-related auditory fields in the expected condition compared to the unexpected condition. This suggests that stimulus-specific event-related activity is dampened for expected tones in auditory cortex. [ABSTRACT FROM AUTHOR]

Fluctuations in functional interactions between brain regions typically occur at the millisecond time scale. Conventional connectivity metrics are not adequately time-resolved to detect such fast fluctuations in functional connectivity. At the same time, attempts to use conventional metrics in a time-resolved manner usually come with the selection of sliding windows of fixed arbitrary length. In the current work, we evaluated the use of high temporal resolution metrics of functional connectivity in conjunction with non-negative tensor factorisation to detect fast fluctuations in connectivity and temporally evolving subnetworks. To this end, we used the phase difference derivative, wavelet coherence , and we also introduced a new metric, the instantaneous amplitude correlation. In order to deal with the inherently noisy nature of magnetoencephalography data and large datasets, we make use of recurrence plots and we used pair-wise orthogonalisation to avoid spurious estimates of functional connectivity due to signal leakage. Firstly, metrics were evaluated in the context of dynamically coupled neural mass models in the presence and absence of delays and also compared to conventional static metrics with fixed sliding windows. Simulations showed that these high temporal resolution metrics outperformed conventional static connectivity metrics. Secondly, the sensitivity of the metrics to fluctuations in connectivity was analysed in post-movement beta rebound magnetoencephalography data, which showed time locked sensorimotor subnetworks that modulated with the post-movement beta rebound. Finally, sensitivity of the metrics was evaluated in resting-state magnetoencephalography, showing similar spatial patterns across metrics, thereby indicating the robustness of the current analysis. The current methods can be applied in cognitive experiments that involve fast modulations in connectivity in relation to cognition. In addition, these methods could also be used as input to temporal graph analysis to further characterise the rapid fluctuation in brain network topology. • Sliding window approaches for dynamic connectivity come with selection of fixed and arbitrary window lengths. • We evaluate the use of high temporal resolution metrics of connectivity for electrophysiological data. • We evaluate two existing measures: the phase difference derivative and the wavelet coherence. • We introduce one new high temporal resolution metric: the instantaneous amplitude correlation. • All metrics can detect genuine fluctuations in dynamic connectivity as was shown in simulations, task- and resting-state data. [ABSTRACT FROM AUTHOR]

Deep brain stimulation (DBS) is an established and effective treatment for several movement disorders and is being developed to treat a host of neuropsychiatric disorders including epilepsy, chronic pain, obsessive compulsive disorder, and depression. However, the neural mechanisms through which DBS produces therapeutic benefits, and in some cases unwanted side effects, in these disorders are only partially understood. Non-invasive neuroimaging techniques that can assess the neural effects of active stimulation are important for advancing our understanding of the neural basis of DBS therapy. Magnetoencephalography (MEG) is a safe, passive imaging modality with relatively high spatiotemporal resolution, which makes it a potentially powerful method for examining the cortical network effects of DBS. However, the degree to which magnetic artifacts produced by stimulation and the associated hardware can be suppressed from MEG data, and the comparability between signals measured during DBS-on and DBS-off conditions, have not been fully quantified. The present study used machine learning methods in conjunction with a visual perception task, which should be relatively unaffected by DBS, to quantify how well neural data can be salvaged from artifact contamination introduced by DBS and how comparable DBS-on and DBS-off data are after artifact removal. Machine learning also allowed us to determine whether the spatiotemporal pattern of neural activity recorded during stimulation are comparable to those recorded when stimulation is off. The spatiotemporal patterns of visually evoked neural fields could be accurately classified in all 8 patients with DBS implants during both DBS-on and DBS-off conditions and performed comparably across those two conditions. Further, the classification accuracy for classifiers trained on the spatiotemporal patterns evoked during DBS-on trials and applied to DBS-off trials, and vice versa, were similar to that of the classifiers trained and tested on either trial type, demonstrating the comparability of these patterns across conditions. Together, these results demonstrate the ability of MEG preprocessing techniques, like temporal signal space separation, to salvage neural data from recordings contaminated with DBS artifacts and validate MEG as a powerful tool to study the cortical consequences of DBS. [ABSTRACT FROM AUTHOR]

The human brain is functionally organized into large-scale neural networks that are dynamically interconnected. Multiple short-lived states of resting-state functional connectivity (rsFC) identified transiently synchronized networks and cross-network integration. However, little is known about the way brain couplings covary as rsFC states wax and wane. In this magnetoencephalography study, we explore the synchronization structure among the spontaneous interactions of well-known resting-state networks (RSNs). To do so, we extracted modes of dynamic coupling that reflect rsFC synchrony and analyzed their spatio-temporal features. These modes identified transient, sporadic rsFC changes characterized by the widespread integration of RSNs across the brain, most prominently in the β band. This is in line with the metastable rsFC state model of resting-state dynamics, wherein our modes fit as state transition processes. Furthermore, the default-mode network (DMN) stood out as being structured into competitive cross-network couplings with widespread DMN-RSN interactions, especially among the β -band modes. These results substantiate the theory that the DMN is a core network enabling dynamic global brain integration in the β band. • We study synchronization among dynamic functional connectivity at rest. • We identify transient modes of dynamic coupling using an ICA of connectivity. • Some α -band modes are longer lived and β -band modes are more widespread. • Modes of the DMN are structured into competitive cross-network couplings. • Results concur with a metastable dynamics and the core network model of the DMN. [ABSTRACT FROM AUTHOR]

• Describes how the choice of ROI extraction method affects the estimation of MEG resting state functional connectivity. • Measures how ROI-based extraction strategies vary across two connectivity measures (ciPLV, PLV) and frequency bands (theta, alpha, beta) in real and simulated data. • Defines the reliability of extraction methods and how it varies according to the size of the ROI. • Finally, it provides practical recommendations for future studies and applications. Magnetoencephalography and electroencephalography (M/EEG) seed-based connectivity analysis typically requires regions of interest (ROI)-based extraction of measures. M/EEG ROI-derived source activity can be treated in different ways. For instance, it is possible to average each ROI's time series prior to calculating connectivity measures. Alternatively one can compute connectivity maps for each element of the ROI, prior to dimensionality reduction to obtain a single map. The impact of these different strategies on connectivity estimation is still unclear. Here, we address this question within a large MEG resting state cohort (N=113) and simulated data. We consider 68 ROIs (Desikan-Kiliany atlas), two measures of connectivity (phase locking value-PLV, and its imaginary counterpart- ciPLV), and three frequency bands (theta 4-8 Hz, alpha 9-12 Hz, beta 15-30 Hz). We consider four extraction methods: (i) mean, or (ii) PCA of the activity within the ROI before computing connectivity, (iii) average, or (iv) maximum connectivity after computing connectivity for each element of the seed. Connectivity outputs from these extraction strategies are then compared with hierarchical clustering, followed by direct contrasts across extraction methods. Finally, the results are validated by using a set of realistic simulations. We show that ROI-based connectivity maps vary remarkably across strategies in both connectivity magnitude and spatial distribution. Dimensionality reduction procedures conducted after computing connectivity are more similar to each-other, while PCA before approach is the most dissimilar to other approaches. Although differences across methods are consistent across frequency bands, they are influenced by the connectivity metric and ROI size. Greater differences were observed for ciPLV than PLV, and in larger ROIs. Realistic simulations confirmed that after aggregation procedures are generally more accurate but have lower specificity (higher rate of false positive connections). Although computationally demanding, after dimensionality reduction strategies should be preferred when higher sensitivity is desired. Given the remarkable differences across aggregation procedures, caution is warranted in comparing results across studies applying different extraction methods. [ABSTRACT FROM AUTHOR]

Multivariate pattern analysis (MVPA) of Magnetoencephalography (MEG) and Electroencephalography (EEG) data is a valuable tool for understanding how the brain represents and discriminates between different stimuli. Identifying the spatial and temporal signatures of stimuli is typically a crucial output of these analyses. Such analyses are mainly performed using linear, pairwise, sliding window decoding models. These allow for relative ease of interpretation, e.g. by estimating a time-course of decoding accuracy, but have limited decoding performance. On the other hand, full epoch multiclass decoding models, commonly used for brain–computer interface (BCI) applications, can provide better decoding performance. However interpretation methods for such models have been designed with a low number of classes in mind. In this paper, we propose an approach that combines a multiclass, full epoch decoding model with supervised dimensionality reduction, while still being able to reveal the contributions of spatiotemporal and spectral features using permutation feature importance. Crucially, we introduce a way of doing supervised dimensionality reduction of input features within a neural network optimised for the classification task, improving performance substantially. We demonstrate the approach on 3 different many-class task-MEG datasets using image presentations. Our results demonstrate that this approach consistently achieves higher accuracy than the peak accuracy of a sliding window decoder while estimating the relevant spatiotemporal features in the MEG signal. • Full-epoch decoding models achieve higher accuracy than sliding window models. • Temporal, spatial, and spectral patterns are extracted from full-epoch models. • A single multiclass model can be used for pairwise decoding without retraining. • Learning the dimensionality reduction of features for decoding improves on PCA. [ABSTRACT FROM AUTHOR]

• A novel M/EEG source imaging method – μ-STAR is proposed to estimate source activities. • Microstate analysis is employed to determine optimal time window length for source imaging. • Spatial constraint and temporal basis functions are used to model source dynamics. • μ-STAR shows superior performance with high spatio-temporal accuracy and fast convergence. Source imaging of Electroencephalography (EEG) and Magnetoencephalography (MEG) provides a noninvasive way of monitoring brain activities with high spatial and temporal resolution. In order to address this highly ill-posed problem, conventional source imaging models adopted spatio-temporal constraints that assume spatial stability of the source activities, neglecting the transient characteristics of M/EEG. In this work, a novel source imaging method μ-STAR that includes a microstate analysis and a spatio-temporal Bayesian model was introduced to address this problem. Specifically, the microstate analysis was applied to achieve automatic determination of time window length with quasi-stable source activity pattern for optimal reconstruction of source dynamics. Then a user-specific spatial prior and data-driven temporal basis functions were utilized to characterize the spatio-temporal information of sources within each state. The solution of the source reconstruction was obtained through a computationally efficient algorithm based upon variational Bayesian and convex analysis. The performance of the μ-STAR was first assessed through numerical simulations, where we found that the determination and inclusion of optimal temporal length in the spatio-temporal prior significantly improved the performance of source reconstruction. More importantly, the μ-STAR model achieved robust performance under various settings (i.e., source numbers/areas, SNR levels, and source depth) with fast convergence speed compared with five widely-used benchmark models (including wMNE, STV, SBL, BESTIES, & SI-STBF). Additional validations on real data were then performed on two publicly-available datasets (including block-design face-processing ERP and continuous resting-state EEG). The reconstructed source activities exhibited spatial and temporal neurophysiologically plausible results consistent with previously-revealed neural substrates, thereby further proving the feasibility of the μ-STAR model for source imaging in various applications. [ABSTRACT FROM AUTHOR]

• This extensive investigation of Rapid Frequency Tagging offers clear suggestions for optimal parameters in order to reliably elicit steady state visual evoked potentials/fields (SSVEP/Fs) that can be recorded using M/EEG while being largely invisible. • We (imperceptibly) flickered patches at various locations on a screen in distinct fast frequencies (>60 Hz) and patch sizes of 1 visual degree and larger which can be combined with any visual information on the screen. • Rapid frequency tagging using these optimal parameters allows for examining a range of cognitive phenomena including spatial attention allocation and potentially clinical use or brain computer interfacing. Frequency tagging has been demonstrated to be a useful tool for identifying representational-specific neuronal activity in the auditory and visual domains. However, the slow flicker (<30 Hz) applied in conventional frequency tagging studies is highly visible and might entrain endogenous neuronal oscillations. Hence, stimulation at faster frequencies that is much less visible and does not interfere with endogenous brain oscillatory activity is a promising new tool. In this study, we set out to examine the optimal stimulation parameters of rapid frequency tagging (RFT/RIFT) with magnetoencephalography (MEG) by quantifying the effects of stimulation frequency, size and position of the flickering patch. Rapid frequency tagging using flickers above 50 Hz results in almost invisible stimulation which does not interfere with slower endogenous oscillations; however, the signal is weaker as compared to tagging at slower frequencies so certainty over the optimal parameters of stimulation delivery are crucial. The here presented results examining the frequency range between 60 Hz and 96 Hz suggest that RFT induces brain responses with decreasing strength up to about 84 Hz. In addition, even at the smallest flicker patch (2°) focally presented RFT induces a significant and measurable oscillatory brain signal (steady state visual evoked potential/field, SSVEP/F) at the stimulation frequency (66 Hz); however, the elicited response increases with patch size. While focal RFT presentation elicits the strongest response, off-centre presentations do generally mainly elicit a measureable response if presented below the horizontal midline. Importantly, the results also revealed considerable individual differences in the neuronal responses to RFT stimulation. Finally, we discuss the comparison of oscillatory measures (coherence and power) and sensor types (planar gradiometers and magnetometers) in order to achieve optimal outcomes. Based on our extensive findings we set forward concrete recommendations for using rapid frequency tagging in human cognitive neuroscience investigations. [Display omitted] [ABSTRACT FROM AUTHOR]

Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. While many studies have investigated static functional connectivity, it has been unclear whether features of dynamic functional connectivity are associated with neurodegenerative diseases. Popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations that prevent extracting reliable features to address this question. Here, we use a novel and robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high resolution magnetoencephalography (MEG) data of participants with Alzheimer's disease (AD) and matched controls. The TVDN algorithm automatically and adaptively learns the low-dimensional spatiotemporal manifold of dynamic RSFC and detects dynamic state transitions in data. We show that amongst all the functional features we investigated, the dynamic manifold features are the most predictive of AD. These include: the temporal complexity of the brain network, given by the number of state transitions and their dwell times, and the spatial complexity of the brain network, given by the number of eigenmodes. These dynamic features have higher sensitivity and specificity in distinguishing AD from healthy subjects than the existing benchmarks do. Intriguingly, we found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. These results indicate that dynamic RSFC features are impacted in neurodegenerative disease like Alzheimer's disease, and may be crucial to understanding the pathophysiological trajectory of these diseases. • The work employs a novel time-varying dynamic network (TVDN) approach to extract the dynamic features from MEG data, circumventing the limitations of conventional sliding-window approaches. • Based on the analysis, we find that AD patients generally have higher spatial complexity, but lower temporal complexity compared with healthy controls. • We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. [ABSTRACT FROM AUTHOR]

The accurate characterization of cortical functional connectivity from Magnetoencephalography (MEG) data remains a challenging problem due to the subjective nature of the analysis, which requires several decisions at each step of the analysis pipeline, such as the choice of a source estimation algorithm, a connectivity metric and a cortical parcellation, to name but a few. Recent studies have emphasized the importance of selecting the regularization parameter in minimum norm estimates with caution, as variations in its value can result in significant differences in connectivity estimates. In particular, the amount of regularization that is optimal for MEG source estimation can actually be suboptimal for coherence-based MEG connectivity analysis. In this study, we expand upon previous work by examining a broader range of commonly used connectivity metrics, including the imaginary part of coherence, corrected imaginary part of Phase Locking Value, and weighted Phase Lag Index, within a larger and more realistic simulation scenario. Our results show that the best estimate of connectivity is achieved using a regularization parameter that is 1 or 2 orders of magnitude smaller than the one that yields the best source estimation. This remarkable difference may imply that previous work assessing source-space connectivity using minimum-norm may have benefited from using less regularization, as this may have helped reduce false positives. Importantly, we provide the code for MEG data simulation and analysis, offering the research community a valuable open source tool for informed selections of the regularization parameter when using minimum-norm for source space connectivity analyses. • Regularization parameter within MNE affects connectivity estimation • The optimal parameter is computed for a large number of synthetic data • In connectivity studies less regularization should be employed with various metrics • Code and data are available open source. [ABSTRACT FROM AUTHOR]

• The absence state of absence seizures might be associated with reduced input of external stimulus. • Stronger delta-band signals have the potential to prevent the initiation of generalized spike-and-wave discharges (GSWDs). • Both the frontal and occipital lobes are crucial in the progression of absence seizures. • The duration of GSWDs could be shortened in the future by enhancing the inhibitory effect of low-frequency signals. Childhood absence epilepsy (CAE) is characterized by impaired consciousness and distinct electroencephalogram (EEG) patterns. However, interictal epileptiform discharges (IEDs) do not lead to noticeable symptoms. This study examines the disparity between ictal and interictal generalized spike-and-wave discharges (GSWDs) to determine the mechanisms behind CAE and consciousness. We enrolled 24 patients with ictal and interictal GSWDs in the study. The magnetoencephalography (MEG) data were recorded before and during GSWDs at a sampling rate of 6000 Hz and analyzed across six frequency bands. The absolute and relative spectral power were estimated with the Minimum Norm Estimate (MNE) combined with the Welch technique. All the statistical analyses were performed using paired-sample tests. During GSWDs, the right lateral occipital cortex indicated a significant difference in the theta band (5–7 Hz) with stronger power (P = 0.027). The interictal group possessed stronger spectral power in the delta band (P < 0.01) and weaker power in the alpha band (P < 0.01) as early as 10 s before GSWDs in absolute and relative spectral power. Additionally, the ictal group revealed enhanced spectral power inside the occipital cortex in the alpha band and stronger spectral power in the right frontal regions within beta (15–29 Hz), gamma 1 (30–59 Hz), and gamma 2 (60–90 Hz) bands. GSWDs seem to change gradually, with local neural activity changing even 10 s before discharge. During GSWDs, visual afferent stimulus insensitivity could be related to the impaired response state in CAE. The inhibitory signal in the low-frequency band can shorten GSWD duration, thereby achieving seizure control through inhibitory effect strengthening. [ABSTRACT FROM AUTHOR]

• Functional connectivity serving cognitive control in healthy aging are understudied. • 78 participants performed the multisource interference task during MEG. • Weaker age-related fronto-visual coupling during flanker interference were observed. • Parietal gamma power differed between Simon and flanker interference conditions. • The study suggests age-related inefficiency in top-down attentional control. The presence of conflicting stimuli adversely affects behavioral outcomes, which could either be at the level of stimulus (Flanker), response (Simon), or both (Multisource). Briefly, flanker interference involves conflicting stimuli requiring selective attention, Simon interference is caused by an incongruity between the spatial location of the task-relevant stimulus and prepotent motor mapping, and multisource is combination of both. Irrespective of the variant, interference resolution necessitates cognitive control to filter irrelevant information and allocate neural resources to task-related goals. Though previously studied in healthy young adults, the direct quantification of changes in oscillatory activity serving such cognitive control and associated inter-regional interactions in healthy aging are poorly understood. Herein, we used an adapted version of the multisource interference task and magnetoencephalography to investigate age-related alterations in the neural dynamics governing both divergent and convergent cognitive interference in 78 healthy participants (age range: 20-66 years). We identified weaker alpha connectivity between bilateral visual and right dorsolateral prefrontal cortices (DLPFC) and left dorsomedial prefrontal cortices (dmPFC), as well as weaker gamma connectivity between bilateral occipital regions and the right dmPFC during flanker interference with advancing age. Further, an age-related decrease in gamma power was observed in the left cerebellum and parietal region for Simon and differential interference effects (i.e., flanker-Simon), respectively. Moreover, the superadditivity model showed decreased gamma power in the right temporoparietal junction (TPJ) with increasing age. Overall, our findings suggest age-related declines in the engagement of top-down attentional control secondary to reduced alpha and gamma coupling between prefrontal and occipital cortices. [ABSTRACT FROM AUTHOR]

The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is non-trivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which cannot be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications. • The work employs the parsimonious spectral graph model (SGM) for brain dynamics. • This work adopts a neural network for SBI of SGM, avoiding complex computations.. • The novel SGM-SBI integration could be a pivotal advance in SC-FC mapping's practical utility. [ABSTRACT FROM AUTHOR]

People with HIV (PWH) often develop HIV-related neurological impairments known as HIV-associated neurocognitive disorder (HAND), but cognitive dysfunction in older PWH may also be due to age-related disorders such as Alzheimer's disease (AD). Discerning these two conditions is challenging since the specific neural characteristics are not well understood and limited studies have probed HAND and AD spectrum (ADS) directly. We examined the neural dynamics underlying motor processing during cognitive interference using magnetoencephalography (MEG) in 22 biomarker-confirmed patients on the ADS, 22 older participants diagnosed with HAND, and 30 healthy aging controls. MEG data were transformed into the time-frequency domain to examine movement-related oscillatory activity and the impact of cognitive interference on distinct stages of motor programming. Both cognitively impaired groups (ADS/HAND) performed significantly worse on the task (e.g., less accurate and slower reaction time) and exhibited reductions in frontal and cerebellar beta and parietal gamma activity relative to controls. Disease-specific aberrations were also detected such that those with HAND exhibited weaker gamma interference effects than those on the ADS in frontoparietal and motor areas. Additionally, temporally distinct beta interference effects were identified, with ADS participants exhibiting stronger beta interference activity in the temporal cortex during motor planning, along with weaker beta interference oscillations dispersed across frontoparietal and cerebellar cortices during movement execution relative to those with HAND. These results indicate both overlapping and distinct neurophysiological aberrations in those with ADS disorders or HAND in key motor and top-down cognitive processing regions during cognitive interference and provide new evidence for distinct neuropathology. • Impairment patterns are similar in early Alzheimer's and HIV-related cognitive decline. • Whether the neural mechanisms underlying these deficits are similar remains unknown. • We utilized MEG and a motor control task to identify common and distinct mechanisms. • The patient groups performed worse than controls and exhibited altered oscillations. • Disease-specific oscillatory aberrations were also found in the beta and gamma range. [ABSTRACT FROM AUTHOR]

Research on how the brain construes meaning during language use has prompted two conflicting accounts. According to the 'grounded view', word understanding involves quick reactivations of sensorimotor (embodied) experiences evoked by the stimuli, with simultaneous or later engagement of multimodal (conceptual) systems integrating information from various sensory streams. Contrariwise, for the 'symbolic view', this capacity depends crucially on multimodal operations, with embodied systems playing epiphenomenal roles after comprehension. To test these contradictory hypotheses, the present magnetoencephalography study assessed implicit semantic access to grammatically constrained action and non-action verbs (n = 100 per category) while measuring spatiotemporally precise signals from the primary motor cortex (M1, a core region subserving bodily movements) and the anterior temporal lobe (ATL, a putative multimodal semantic hub). Convergent evidence from sensor- and source-level analyses revealed that increased modulations for action verbs occurred earlier in M1 (∼130–190 ms) than in specific ATL hubs (∼250–410 ms). Moreover, machine-learning decoding showed that trial-by-trial classification peaks emerged faster in M1 (∼100–175 ms) than in the ATL (∼345–500 ms), with over 71% accuracy in both cases. Considering their latencies, these results challenge the 'symbolic view' and its implication that sensorimotor mechanisms play only secondary roles in semantic processing. Instead, our findings support the 'grounded view', showing that early semantic effects are critically driven by embodied reactivations and that these cannot be reduced to post-comprehension epiphenomena, even when words are individually classified. Briefly, our study offers non-trivial insights to constrain fine-grained models of language and understand how meaning unfolds in neural time. [ABSTRACT FROM AUTHOR]

We introduce two Convolutional Neural Network (CNN) classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements. Network design follows a generative model of the electromagnetic (EEG and MEG) brain signals allowing explorative analysis of neural sources informing classification. The proposed networks outperform traditional classifiers as well as more complex neural networks when decoding evoked and induced responses to different stimuli across subjects. Importantly, these models can successfully generalize to new subjects in real-time classification enabling more efficient brain–computer interfaces (BCI). • We introduce neural-network classifiers optimized for electromagnetic brain signals. • Our models outperform other classifiers/methods in across-subject classification. • Online updating/adaptation enables efficient brain–computer interfaces. • The trained model is interpretable in neurophysiological terms. • Implementation is publicly available. [ABSTRACT FROM AUTHOR]

Frequency tagging has been widely used to study the role of visual selective attention. Presenting a visual stimulus flickering at a specific frequency generates so-called steady-state visually evoked responses. However, frequency tagging is mostly done at lower frequencies (<30 Hz). This produces a visible flicker, potentially interfering with both perception and neuronal oscillations in the theta, alpha and beta band. To overcome these problems, we used a newly developed projector with a 1440 Hz refresh rate allowing for frequency tagging at higher frequencies. We asked participants to perform a cued spatial attention task in which imperative pictorial stimuli were presented at 63 Hz or 78 Hz while measuring whole-head magnetoencephalography (MEG). We found posterior sensors to show a strong response at the tagged frequency. Importantly, this response was enhanced by spatial attention. Furthermore, we reproduced the typical modulations of alpha band oscillations, i.e., decrease in the alpha power contralateral to the attentional cue. The decrease in alpha power and increase in frequency tagged signal with attention correlated over subjects. We hereby provide proof-of-principle for the use of high-frequency tagging to study sensory processing and neuronal excitability associated with attention. [ABSTRACT FROM AUTHOR]

Listeners are often challenged by adverse listening conditions during language comprehension induced by external factors, such as noise, but also internal factors, such as being a non-native listener. Visible cues, such as semantic information conveyed by iconic gestures, can enhance language comprehension in such situations. Using magnetoencephalography (MEG) we investigated whether spatiotemporal oscillatory dynamics can predict a listener's benefit of iconic gestures during language comprehension in both internally (non-native versus native listeners) and externally (clear/degraded speech) induced adverse listening conditions. Proficient non-native speakers of Dutch were presented with videos in which an actress uttered a degraded or clear verb, accompanied by a gesture or not, and completed a cued-recall task after every video. The behavioral and oscillatory results obtained from non-native listeners were compared to an MEG study where we presented the same stimuli to native listeners (Drijvers et al., 2018a). Non-native listeners demonstrated a similar gestural enhancement effect as native listeners, but overall scored significantly slower on the cued-recall task. In both native and non-native listeners, an alpha/beta power suppression revealed engagement of the extended language network, motor and visual regions during gestural enhancement of degraded speech comprehension, suggesting similar core processes that support unification and lexical access processes. An individual's alpha/beta power modulation predicted the gestural benefit a listener experienced during degraded speech comprehension. Importantly, however, non-native listeners showed less engagement of the mouth area of the primary somatosensory cortex, left insula (beta), LIFG and ATL (alpha) than native listeners, which suggests that non-native listeners might be hindered in processing the degraded phonological cues and coupling them to the semantic information conveyed by the gesture. Native and non-native listeners thus demonstrated similar yet distinct spatiotemporal oscillatory dynamics when recruiting visual cues to disambiguate degraded speech. • Non-native listeners benefit from gestures to access speech in noise. • Alpha/beta power suppression reflects engagement of task-relevant brain regions. • An individual's alpha/beta power modulation predicts gestural benefit. • Non-native listeners engage mouth area of primary sensory cortex less than natives. • Non-native listeners engage LIFG and ATL less than natives during comprehension. [ABSTRACT FROM AUTHOR]

The spatial resolution of magnetoencephalography (MEG) can be increased from that of conventional SQUID-based systems by employing on-scalp sensor arrays of e.g. optically-pumped magnetometers (OPMs). However, OPMs reach sufficient sensitivity for neuromagnetic measurements only when operated in a very low absolute magnetic field of few nanoteslas or less, usually not reached in a typical magnetically shielded room constructed for SQUID-based MEG. Moreover, field drifts affect the calibration of OPMs. Static and dynamic suppression of interfering fields is thus necessary for good-quality neuromagnetic measurements with OPMs. Here, we describe an on-scalp MEG system that utilizes OPMs and external compensation coils that provide static and dynamic shielding against ambient fields. In a conventional two-layer magnetically shielded room, our coil system reduced the maximum remanent DC-field component within an 8-channel OPM array from 70 to less than 1 nT, enabling the sensors to operate in the sensitive spin exchange relaxation-free regime. When compensating field drifts below 4 Hz, a low-frequency shielding factor of 22 dB was achieved, which reduced the peak-to-peak drift from 1.3 to 0.4 nT and thereby the standard deviation of the sensor calibration from 1.7% to 0.5%. Without band-limiting the field that was compensated, a low-frequency shielding factor of 43 dB was achieved. We validated the system by measuring brain responses to electric stimulation of the median nerve. With dynamic shielding and digital interference suppression methods, single-trial somatosensory evoked responses could be detected. Our results advance the deployment of OPM-based on-scalp MEG in lighter magnetic shields. • We present an on-scalp MEG system based on optically-pumped magnetometers (OPMs). • The system uses active, dynamic shielding with external coils. • Active shielding reduces OPM calibration errors. • We demonstrate the operation of the system by measuring somatosensory responses. • Our solution facilitates deployment of OPM-based MEG in lighter magnetic shields. [ABSTRACT FROM AUTHOR]

Abstract Imagination is an internally-generated process, where one can make oneself or other people appear as protagonists of a scene. How does the brain tag the protagonist of an imagined scene as being oneself or someone else? Crucially, during imagination, neither external stimuli nor motor feedback are available to disentangle imagining oneself from imagining someone else. Here, we test the hypothesis that an internal mechanism based on the neural monitoring of heartbeats could distinguish between self and other. 23 participants imagined themselves (from a first-person perspective) or a friend (from a third-person perspective) in various scenarios, while their brain activity was recorded with magnetoencephalography and their cardiac activity was simultaneously monitored. We measured heartbeat-evoked responses, i.e. transients of neural activity occurring in response to each heartbeat, during imagination. The amplitude of heartbeat-evoked responses differed between imagining oneself and imagining a friend, in the precuneus and posterior cingulate regions bilaterally. Effect size was modulated by the daydreaming frequency scores of participants but not by their interoceptive abilities. These results could not be accounted for by other characteristics of imagination (e.g., the ability to adopt the perspective, valence or arousal), nor by cardiac parameters (e.g., heart rate) or arousal levels (e.g. arousal ratings, pupil diameter). Heartbeat-evoked responses thus appear as a neural marker distinguishing self from other during imagination. Highlights • Heartbeat-evoked responses differentiate self from other during imagination. • These effects were located in the precuneus and posterior cingulate. • The neural monitoring of the heart could be a mechanism for self/other distinction. [ABSTRACT FROM AUTHOR]

Abstract Hemispheric asymmetries play an important role in multiple cerebral functions. Asymmetries in prefrontal cortex (PFC) function have been suggested to regulate emotional processing in that right-hemispheric dominance biases towards negative affect, whereas left PFC dominance favors positive affect. This study used transcranial magnetic stimulation to test the causal role of prefrontal asymmetries in the processing of emotional stimuli. To experimentally induce hemispheric asymmetries, 21 healthy volunteers underwent two separate sessions of inhibitory continuous theta burst stimulation (cTBS) to the left versus right dorsolateral prefrontal cortex. Each stimulation was followed by magnetoencephalographic (MEG) recordings of event-related fields elicited by visually presented emotional words in a silent reading task and a subsequent behavioral emotion categorization task. The asymmetry manipulation influenced valence processing of words in early, mid-latency and late time intervals in right occipitotemporal and parietal brain regions. Left-sided cTBS (inducing right-hemispheric dominance) consistently resulted in enhanced brain responses to negative words, while right-sided cTBS (inducing left-hemispheric dominance) enhanced responses to positive words. On a behavioral level, right-hemispheric dominance resulted in more categorization matches of negative compared to positive words, while left-hemispheric dominance resulted in reverse effects. These results provide direct evidence that bottom-up valence processing is influenced by prefrontal hemispheric asymmetry. Highlights • TMS-induced prefrontal hemispheric asymmetry controls valence processing. • Asymmetry controls valence-specific MEG correlates of emotional attention. • The modulatory influence starts 80 ms after stimulus onset. • Right over left prefrontal dominance increases negative valence – and vice versa. • Prefrontal hemispheric asymmetry can be shifted by non-invasive stimulation. [ABSTRACT FROM AUTHOR]

Abstract Magnetoencephalography (MEG) is a non-invasive neuroimaging technique that provides whole-head measures of neural activity with millisecond temporal resolution. Over the last three decades, MEG has been used for assessing brain activity, most commonly in adults. MEG has been used less often to examine neural function during early development, in large part due to the fact that infant whole-head MEG systems have only recently been developed. In this review, an overview of infant MEG studies is provided, focusing on the period from birth to three years. The advantages of MEG for measuring neural activity in infants are highlighted (See Box 1), including the ability to assess activity in brain (source) space rather than sensor space, thus allowing direct assessment of neural generator activity. Recent advances in MEG hardware and source analysis are also discussed. As the review indicates, efforts in this area demonstrate that MEG is a promising technology for studying the infant brain. As a noninvasive technology, with emerging hardware providing the necessary sensitivity, an expected deliverable is the capability for longitudinal infant MEG studies evaluating the developmental trajectory (maturation) of neural activity. It is expected that departures from neuro-typical trajectories will offer early detection and prognosis insights in infants and toddlers at-risk for neurodevelopmental disorders, thus paving the way for early targeted interventions. Highlights • MEG provides real-time (msec) measures of neural activity in infants. • MEG signals can be modeled in source (brain) space. • MEG can detect oscillations in the infant brain – probes for connectivity. • Emerging MEG hardware provides increased sensitivity to infant brain activity. [ABSTRACT FROM AUTHOR]

Abstract The sensation of touch in the glabrous skin of the human hand is conveyed by thousands of fast-conducting mechanoreceptive afferents, which can be categorised into four distinct types. The spiking properties of these afferents in the periphery in response to varied tactile stimuli are well-characterised, but relatively little is known about the spatiotemporal properties of the neural representations of these different receptor types in the human cortex. Here, we use the novel methodological combination of single-unit intraneural microstimulation (INMS) with magnetoencephalography (MEG) to localise cortical representations of individual touch afferents in humans, by measuring the extracranial magnetic fields from neural currents. We found that by assessing the modulation of the beta (13–30 Hz) rhythm during single-unit INMS, significant changes in oscillatory amplitude occur in the contralateral primary somatosensory cortex within and across a group of fast adapting type I mechanoreceptive afferents, which corresponded well to the induced response from matched vibrotactile stimulation. Combining the spatiotemporal specificity of MEG with the selective single-unit stimulation of INMS enables the interrogation of the central representations of different aspects of tactile afferent signalling within the human cortices. The fundamental finding that single-unit INMS ERD responses are robust and consistent with natural somatosensory stimuli will permit us to more dynamically probe the central nervous system responses in humans, to address questions about the processing of touch from the different classes of mechanoreceptive afferents and the effects of varying the stimulus frequency and patterning. Highlights • We combine MEG with single unit INMS to image cortical responses to stimulation of the fundaments of touch. • Stimulating individual fast adapting type I mechanoreceptors in the hand shows changes in beta oscillatory power in S1. • Comparing results to vibrotactile stimulation experiments show the results to be as robust as conventional stimuli. • Imaging responses to INMS could allow us to test emerging theories about beta burst events. [ABSTRACT FROM AUTHOR]

Abstract It is well known that attentional selection of relevant information relies on local synchronization of alpha band neuronal oscillations in visual cortices for inhibition of distracting inputs. Additionally, evidence for long-range coupling of neuronal oscillations between visual cortices and regions engaged in the anticipation of upcoming stimuli has been more recently provided. Nevertheless, on the one hand the relation between long-range functional coupling and anatomical connections is still to be assessed, and, on the other hand, the specific role of the alpha and beta frequency bands in the different processes underlying visuo-spatial attention still needs further clarification. We address these questions using measures of linear (frequency-specific) and nonlinear (cross-frequency) phase-synchronization in a cohort of 28 healthy subjects using magnetoencephalography. We show that alpha band phase-synchronization is modulated by the orienting of attention according to a parieto-occipital top-down mechanism reflecting behavior, and its hemispheric asymmetry is predicted by volume's asymmetry of specific tracts of the Superior-Longitudinal-Fasciculus. We also show that a network comprising parietal regions and the right putative Frontal-Eye-Field, but not the left, is recruited in the deployment of spatial attention through an alpha-beta cross-frequency coupling. Overall, we demonstrate that the visuospatial attention network features subsystems indexed by characteristic spectral fingerprints, playing different functional roles in the anticipation of upcoming stimuli and with diverse relation to fiber tracts. Highlights • Orienting visuo-spatial attention modulates parieto-occipital alpha synchronization. • Hemispheric asymmetry of alpha connectivity is predicted by asymmetry of SLF. • Hemispheric asymmetry of alpha connectivity reflects subject's performance. • Alpha-beta cross-frequency coupling indexes attention deployment but not orienting. [ABSTRACT FROM AUTHOR]

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

Abstract Functional networks obtained from magnetoencephalography (MEG) from different frequency bands show distinct spatial patterns. It remains to be elucidated how distinct spatial patterns in MEG networks emerge given a single underlying structural network. Recent work has suggested that the eigenmodes of the structural network might serve as a basis set for functional network patterns in the case of functional MRI. Here, we take this notion further in the context of frequency band specific MEG networks. We show that a selected set of eigenmodes of the structural network can predict different frequency band specific networks in the resting state, ranging from delta (1–4 Hz) to the high gamma band (40–70 Hz). These predictions outperform predictions based from surrogate data, suggesting a genuine relationship between eigenmodes of the structural network and frequency specific MEG networks. We then show that the relevant set of eigenmodes can be excited in a network of neural mass models using linear stability analysis only by including delays. Excitation of an eigenmode in this context refers to a dynamic instability of a network steady state to a spatial pattern with a corresponding coherent temporal oscillation. Simulations verify the results from linear stability analysis and suggest that theta, alpha and beta band networks emerge very near to the bifurcation. The delta and gamma bands in the resting state emerges further away from the bifurcation. These results show for the first time how delayed interactions can excite the relevant set of eigenmodes that give rise to frequency specific functional connectivity patterns. Highlights • The first six eigenmodes of the structural network form a basis set for different frequency band specific MEG networks. • Eigenmodes can be excited at the functional network level in a linearised system of neural masses with and without delays. • Offset and axonal delays are required to excite the correct set of eigenmodes at the network bifurcation. • Alpha, Beta and Theta band MEG networks emerge right after the network bifurcation. • Gamma and Delta band MEG networks emerge well after the network bifurcation. [ABSTRACT FROM AUTHOR]

Abstract The ability to dynamically allocate neural resources within the visual space is supported by a number of spectrally-specific oscillatory responses, and such visuospatial processing has been found to decline moderately with age and differ by sex. However, the direct effects of age and sex on these oscillatory dynamics remains poorly understood. Using magnetoencephalography (MEG), structural magnetic resonance imaging, and advanced source reconstruction and statistical methods, we investigated the impact of aging and sex on behavioral performance and the underlying neural dynamics during visuospatial processing. In a large sample spanning a broad age range, we find that a number of prototypical attention and perception network components, both spectrally- and spatially-defined, exhibit complex and uniquely informative relationships with age and sex. Specifically, neural responses in the theta range (4–10 Hz) were found to covary with chronological age in prefrontal and motor cortices, signifying a possible relationship between age and cognitive control. Further, we found that beta (18–24 Hz) activity covaried with age across a large swath of the somato-motor strip, supporting previous findings of motor planning and execution deficits with increasing age. Finally, gamma-frequency (48–70 Hz) oscillations were found to exhibit robust covariance with age in superior parietal and temporo-parietal areas, indicating that the mapping of saliency in visual space is modulated by the normal aging process. Interestingly, behavioral performance and some of these oscillatory neural responses also exhibited interactions between age and sex, indicating sex differences in the evolution of the neural coding of visual perception as age increases. In particular, men were found to have stronger correlations between age and neural oscillatory responses during task performance than women in lateral occipital and superior temporal regions in the alpha band and in dorsolateral prefrontal cortex in the gamma band, while women exhibited more robust covariance between age and neural responses than men in inferior temporal and medial prefrontal cortex in the theta range. Highlights • Healthy adults spanning a broad age range performed a visuospatial task during MEG. • Participants exhibited theta, alpha, beta, and gamma neural responses to the task. • Theta and gamma oscillations covaried with age in frontal and parietal cortices. • Gamma activity differed by biological sex in parietal and cerebellar regions. • Multi-spectral age/sex interactions occurred in occipital and prefrontal areas. [ABSTRACT FROM AUTHOR]

Abstract Development of cognitive functions and the underlying neurophysiology is evident throughout childhood and adolescence, with higher order processes such as working memory (WM) being some of the last cognitive faculties to fully mature. Previous functional neuroimaging studies of the neurodevelopment of WM have largely focused on overall regional activity levels rather than the temporal dynamics of neural component recruitment. In this study, we used magnetoencephalography (MEG) to examine the neural dynamics of WM in a large cohort of children and adolescents who were performing a high-load, modified verbal Sternberg WM task. Consistent with previous studies in adults, our findings indicated left-lateralized activity throughout the task period, beginning in the occipital cortices and spreading anterior to include temporal and prefrontal cortices during later encoding and into maintenance. During maintenance, the occipital alpha increase that has been widely reported in adults was found to be relatively weak in this developmental sample, suggesting continuing development of this component of neural processing, which was supported by correlational analyses. Intriguingly, we also found sex-specific developmental effects in alpha responses in the right inferior frontal region during encoding and in parietal and occipital cortices during maintenance. These findings suggested a developmental divergence between males and females in the maturation of neural circuitry serving WM during the transition from childhood to adolescence. Highlights • A large cohort of 9–14 year-olds completed a verbal working memory task during MEG. • Behavioral performance on the working memory task improved with age. • Left-lateralized alpha responses were dynamic based on the phase of the task. • Developmental effects were sex-specific for several key oscillatory responses. [ABSTRACT FROM AUTHOR]

Abstract Neurofeedback requires a direct translation of neuronal brain activity to sensory input given to the user or subject. However, decoding certain states, e.g., mindfulness or wandering thoughts, from ongoing brain activity remains an unresolved problem. In this study, we used magnetoencephalography (MEG) to acquire brain activity during mindfulness meditation and thought-inducing tasks mimicking wandering thoughts. We used a novel real-time feature extraction to decode the mindfulness, i.e., to discriminate it from the thought-inducing tasks. The key methodological novelty of our approach is usage of MEG power spectra and functional connectivity of independent components as features underlying mindfulness states. Performance was measured as the classification accuracy on a separate session but within the same subject. We found that the spectral- and connectivity-based classification approaches allowed discriminating mindfulness and thought-inducing tasks with an accuracy around 60% compared to the 50% chance-level. Both classification approaches showed similar accuracy, although the connectivity approach slightly outperformed the spectral one in a few cases. Detailed analysis showed that the classification coefficients and the associated independent components were highly individual among subjects and a straightforward transfer of the coefficients over subjects provided near chance-level classification accuracy. Thus, discriminating between mindfulness and wandering thoughts seems to be possible, although with limited accuracy, by machine learning, especially on the subject-level. Our hope is that the developed spectral- and connectivity-based decoding methods can be utilized in real-time neurofeedback to decode mindfulness states from ongoing neuronal activity, and hence, provide a basis for improved, individualized mindfulness training. [ABSTRACT FROM AUTHOR]

Abstract Numerous studies of motor control have confirmed beta and gamma oscillations in the primary motor cortices during basic movements. These responses include a robust beta decrease that precedes and extends through movement onset, a transient gamma response that coincides with the movement, and a post-movement beta rebound (PMBR) response that occurs after movement offset. While the existence of these responses has been confirmed by many studies, very few studies have examined their developmental trajectory. In the current study, we utilized magnetoencephalography (MEG) to investigate age-related changes in sensorimotor cortical oscillations in a large cross-section of children and adolescents (n = 94; age range = 9 -15 years-old). All participants performed a stimulus detection task with their right finger and the resulting MEG data were examined using oscillatory analysis methods and imaged using a beamformer. Consistent with adult studies, these youth participants exhibited characteristic beta (16–24 Hz) decreases prior to and during movement, as well as PMBR responses following movement offset, and a transient gamma (74–84 Hz) response during movement execution. Our primary findings were that the strength of the PMBR increased with age, while the strength of the gamma synchronization decreased with chronological age. In addition, the strength of each motor-related oscillatory response was significantly correlated with the power of spontaneous activity in the same frequency range and same voxel. This was the case for all three oscillatory responses. In conclusion, we investigated motor-related oscillatory activity in the largest cohort of children and adolescents reported to date, and our results indicated that beta and gamma cortical oscillations continue to develop as children transition into adolescents, and that these responses may not be fully matured until young to middle adulthood. Highlights • A large cohort of 94 healthy youth completed a motor control task during MEG. • MEG data were analyzed using oscillatory methods and imaged using a beamformer. • The strength of movement-related gamma and post-movement beta were modulated by age. • Spontaneous baseline power was correlated with the strength of beta and gamma oscillations. • Motor-related neural oscillations continue to develop throughout childhood. [ABSTRACT FROM AUTHOR]

Abstract Low frequency oscillations such as alpha (8–12 Hz) are hypothesized to rhythmically gate sensory processing, reflected by 40–100 Hz gamma band activity, via the mechanism of pulsed inhibition. We applied transcranial alternating current stimulation (TACS) at individual alpha frequency (IAF) and flanking frequencies (IAF-4 Hz, IAF+4 Hz) to the occipital cortex of healthy human volunteers during concurrent magnetoencephalography (MEG), while participants performed a visual detection task inducing strong gamma-band responses. Occipital (but not retinal) TACS phasically suppressed stimulus-induced gamma oscillations in the visual cortex and impaired target detection, with stronger phase-to-amplitude coupling predicting behavioral impairments. Retinal control TACS ruled out retino-thalamo-cortical entrainment resulting from (subthreshold) retinal stimulation. All TACS frequencies tested were effective, suggesting that visual gamma-band responses can be modulated by a range of low frequency oscillations. We propose that TACS-induced membrane potential modulations mimic the rhythmic change in cortical excitability by which spontaneous low frequency oscillations may eventually exert their impact when gating sensory processing via pulsed inhibition. Highlights • Occipital TACS at individual alpha frequency ±4 Hz applied during MEG recording. • Visual stimulus-induced gamma responses assessed during visual detection task. • Retinal control montage ruled out retino-thalamo-cortical stimulation as confound. • TACS phasically suppressed stimulus-induced gamma power and impaired perception. • TACS-induced phasic gamma suppression predicted decline in perceptual performance. [ABSTRACT FROM AUTHOR]

Abstract A network of predominantly left-lateralized brain regions has been linked to verbal working memory (VWM) performance. However, the impact of memory load on the oscillatory dynamics serving VWM is far less understood. To further investigate this, we had 26 healthy adults perform a high-load (6 letter) and low-load (4 letter) variant of a VWM task while undergoing magnetoencephalography (MEG). MEG data were evaluated in the time-frequency domain and significant oscillatory responses spanning the encoding and maintenance phases were reconstructed using a beamformer. To determine the impact of load on the neural dynamics, the resulting images were examined using paired-samples t -tests and virtual sensor analyses. Our results indicated stronger increases in frontal theta activity in the high- relative to low-load condition during early encoding. Stronger decreases in alpha/beta activity were also observed during encoding in bilateral posterior cortices during the high-load condition, and the strength of these load effects increased as encoding progressed. During maintenance, stronger decreases in alpha activity in the left inferior frontal gyrus, middle temporal gyrus, supramarginal gyrus, and inferior parietal cortices were detected during high- relative to low-load performance, with the strength of these load effects remaining largely static throughout maintenance. Finally, stronger increases in occipital alpha activity were observed during maintenance in the high-load condition, and the strength of these effects grew stronger with time during the first half of maintenance, before dissipating during the latter half of maintenance. Notably, this was the first study to utilize a whole-brain approach to statistically evaluate the temporal dynamics of load-related oscillatory differences during encoding and maintenance processes, and our results highlight the importance of spatial, temporal, and spectral specificity in this regard. Highlights • The impact of load on working memory-related oscillations has been inconsistent. • Adults performed a load-varying verbal working memory task during MEG. • MEG data were subjected to a beamformer and advanced oscillatory analysis methods. • Load distinctly modulated behavior, encoding- and maintenance-related oscillations. • Load-related effects were dynamic and involved a network of left cortical regions. [ABSTRACT FROM AUTHOR]

Abstract During connected speech listening, brain activity tracks speech rhythmicity at delta (∼0.5 Hz) and theta (4–8 Hz) frequencies. Here, we compared the potential of magnetoencephalography (MEG) and high-density electroencephalography (EEG) to uncover such speech brain tracking. Ten healthy right-handed adults listened to two different 5-min audio recordings, either without noise or mixed with a cocktail-party noise of equal loudness. Their brain activity was simultaneously recorded with MEG and EEG. We quantified speech brain tracking channel-by-channel using coherence, and with all channels at once by speech temporal envelope reconstruction accuracy. In both conditions, speech brain tracking was significant at delta and theta frequencies and peaked in the temporal regions with both modalities (MEG and EEG). However, in the absence of noise, speech brain tracking estimated from MEG data was significantly higher than that obtained from EEG. Furthemore, to uncover significant speech brain tracking, recordings needed to be ∼3 times longer in EEG than MEG, depending on the frequency considered (delta or theta) and the estimation method. In the presence of noise, both EEG and MEG recordings replicated the previous finding that speech brain tracking at delta frequencies is stronger with attended speech (i.e., the sound subjects are attending to) than with the global sound (i.e., the attended speech and the noise combined). Other previously reported MEG findings were replicated based on MEG but not EEG recordings: 1) speech brain tracking at theta frequencies is stronger with attended speech than with the global sound, 2) speech brain tracking at delta frequencies is stronger in noiseless than noisy conditions, and 3) when noise is added, speech brain tracking at delta frequencies dampens less in the left hemisphere than in the right hemisphere. Finally, sources of speech brain tracking reconstructed from EEG data were systematically deeper and more posterior than those derived from MEG. The present study demonstrates that speech brain tracking is better seen with MEG than EEG. Quantitatively, EEG recordings need to be ∼3 times longer than MEG recordings to uncover significant speech brain tracking. As a consequence, MEG appears more suited than EEG to pinpoint subtle effects related to speech brain tracking in a given recording time. Highlights • Speech brain tracking was analysed from simultaneous MEG and EEG data. • Uncovering speech brain tracking requires 3 times shorter MEG than EEG recordings. • Some previous MEG findings were replicated with MEG but not with EEG. [ABSTRACT FROM AUTHOR]

Abstract The brain builds up expectations to future events based on the patterns of past events. This function has been studied extensively in the auditory and visual domains using various oddball paradigms, but only little exploration of this phenomenon has been done in the somatosensory domain. In this study, we explore how expectations of somatosensory stimulations are established and expressed in neural activity as measured with magnetoencephalography. Using tactile stimulations to the index finger, we compared conditions with actual stimulation to conditions with omitted stimulations , both of which were either expected or unexpected. Our results show that when a stimulation is expected but omitted, a time-locked response occurs ∼135 ms subsequent to the expected stimulation. This somatosensory response to "nothing" was source localized to the secondary somatosensory cortex and to the insula. This provides novel evidence of the capability of the brain of millisecond time-keeping of somatosensory patterns across intervals of 3000 ms. Our results also show that when stimuli are repeated and expectations are established, there is associated activity in the theta and beta bands. These theta and beta band expressions of expectation were localized to the primary somatosensory area, inferior parietal cortex and cerebellum. Furthermore, there was gamma band activity in the right insula for the first stimulation after an omission, which indicates the detection of a new stimulation event after an expected pattern has been broken. Finally, our results show that cerebellum play a crucial role in predicting upcoming stimulation and in predicting when stimulation may begin again. [ABSTRACT FROM AUTHOR]

Characterizing the precise computations carried out by the various nodes of the semantic network remains a central challenge. One of the better understood nodes within this system is the left anterior temporal lobe (LATL), which shows an early (∼250 ms) amplitude increase if the semantic composition between the current word and its context is in some ways "simple." As this type of effect has only been demonstrated for noun-modifier composition, we asked if a similar pattern is elicited for verb phrase composition. Agentive, resultative, and eventive adverbs were employed to vary whether the meaning of the adverb directly applies to the verb or not, with only eventives exemplifying direct and straightforward composition. Results showed that eventives, but not agentives or resultatives, elicited a significant increase in the LATL at 250 ms. The RATL showed a sharply contrasting pattern, with agentives showing the largest activity, suggesting a distinct role in semantic composition. [ABSTRACT FROM AUTHOR]

Abstract Increasing evidence suggests that neuronal communication is a defining property of functionally specialized brain networks and that it is implemented through synchronization between population activities of distinct brain areas. The detection of long-range coupling in electroencephalography (EEG) and magnetoencephalography (MEG) data using conventional metrics (such as coherence or phase-locking value) is by definition contaminated by spatial leakage. Methods such as imaginary coherence, phase-lag index or orthogonalized amplitude correlations tackle spatial leakage by ignoring zero-phase interactions. Although useful, these metrics will by construction lead to false negatives in cases where true zero-phase coupling exists in the data and will underestimate interactions with phase lags in the vicinity of zero. Yet, empirically observed neuronal synchrony in invasive recordings indicates that it is not uncommon to find zero or close-to-zero phase lag between the activity profiles of coupled neuronal assemblies. Here, we introduce a novel method that allows us to mitigate the undesired spatial leakage effects and detect zero and near zero phase interactions. To this end, we propose a projection operation that operates on sensor-space cross-spectrum and suppresses the spatial leakage contribution but retains the true zero-phase interaction component. We then solve the network estimation task as a source estimation problem defined in the product space of interacting source topographies. We show how this framework provides reliable interaction detection for all phase-lag values and we thus refer to the method as Phase Shift Invariant Imaging of Coherent Sources (PSIICOS). Realistic simulations demonstrate that PSIICOS has better detector characteristics than existing interaction metrics. Finally, we illustrate the performance of PSIICOS by applying it to real MEG dataset recorded during a standard mental rotation task. Taken together, using analytical derivations, data simulations and real brain data, this study presents a novel source-space MEG/EEG connectivity method that overcomes previous limitations and for the first time allows for the estimation of true zero-phase coupling via non-invasive electrophysiological recordings. Highlights • Novel framework for linear functional coupling estimation. • Suppresses spatial leakage and yet keeps zero-phase lag coupling detectable. • Yields uniform performance over the entire range of phase lags. • Renders coupling detection task as a source estimation problem but in hyper-space. • May add robustness to functional coupling studies and welcomes priors. [ABSTRACT FROM AUTHOR]

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

Abstract Small, commercially-available Optically Pumped Magnetometers (OPMs) can be used to construct a wearable Magnetoencephalography (MEG) system that allows large head movements to be made during recording. The small dynamic range of these sensors however means that movement in the residual static magnetic field found inside typical Magnetically Shielded Rooms (MSRs) can saturate the sensor outputs, rendering the data unusable. This problem can be ameliorated by using a set of electromagnetic coils to attenuate the spatially-varying remnant field. Here, an array of bi-planar coils, which produce an open and accessible scanning environment, was designed and constructed. The coils were designed using a harmonic minimisation method previously used for gradient coil design in Magnetic Resonance Imaging (MRI). Six coils were constructed to null B x , B y and B z as well as the three dominant field gradients d B x / d z , d B y / d z and d B z / d z. The coils produce homogeneous (within ±5%) fields or field gradients over a volume of 40 × 40 × 40 cm3. This volume is sufficient to contain an array of OPMs, mounted in a 3D-printed scanner-cast, during basic and natural movements. Automated control of the coils using reference sensor measurements allows reduction of the largest component of the static field (B x) from 21.8 ± 0.2 nT to 0.47 ± 0.08 nT. The largest gradient (d B x / d z) was reduced from 7.4 nT/m to 0.55 nT/m. High precision optical tracking allowed experiments involving controlled and measured head movements, which revealed that a rotation of the scanner-cast by ±34° and translation of ±9.7 cm of the OPMs in this field generated only a 1 nT magnetic field variation across the OPM array, when field nulling was applied. This variation could be further reduced to 0.04 nT by linear regression of field variations that were correlated with the measured motion parameters. To demonstrate the effectiveness of the bi-planar coil field cancellation system in a real MEG experiment, a novel measurement of retinotopy was investigated, where the stimulus remains fixed and head movements made by the subject shift the visual presentation to the lower left or right quadrants of the field of view. Left and right visual field stimulation produced the expected responses in the opposing hemisphere. This simple demonstration shows that the bi-planar coil system allows accurate OPM-MEG recordings to be made on an unrestrained subject. Highlights • The design and use of bi-planar coils for magnetic field nulling is described. • Field nulling allows large subject movements during onscalp MEG recordings. • Optical tracking shows high quality data can be acquired during these movements. • A novel measurement of retinotopy where the subject moves their head is shown. [ABSTRACT FROM AUTHOR]

Abstract Recent work has demonstrated that Optically Pumped Magnetometers (OPMs) can be utilised to create a wearable Magnetoencephalography (MEG) system that is motion robust. In this study, we use this system to map eloquent cortex using a clinically validated language lateralisation paradigm (covert verb generation: 120 trials, ∼10 min total duration) in healthy adults (n = 3). We show that it is possible to lateralise and localise language function on a case by case basis using this system. Specifically, we show that at a sensor and source level we can reliably detect a lateralising beta band (15–30 Hz) desynchronization in all subjects. This is the first study of human cognition using OPMs and not only highlights this technology's utility as tool for (developmental) cognitive neuroscience but also its potential to contribute to surgical planning via mapping of eloquent cortex, especially in young children. Highlights • First cognitive neuroscience experiment using optically pumped magnetometers. • Language lateralisation is feasible with optically pumped magnetometers. • Robust within-subject effects at sensor and source level. [ABSTRACT FROM AUTHOR]

Abstract In magnetoencephalography (MEG) research there are a variety of inversion methods to transform sensor data into estimates of brain activity. Each new inversion scheme is generally justified against a specific simulated or task scenario. The choice of this scenario will however have a large impact on how well the scheme performs. We describe a method with minimal selection bias to quantify algorithm performance using human resting state data. These recordings provide a generic, heterogeneous, and plentiful functional substrate against which to test different MEG recording and reconstruction approaches. We used a Hidden Markov model to spatio-temporally partition data into self-similar dynamic states. To test the anatomical precision that could be achieved, we then inverted these data onto libraries of systematically distorted subject-specific cortical meshes and compared the quality of the fit using cross validation and a Free energy metric. This revealed which inversion scheme was able to identify the least distorted (most accurate) anatomical models, and allowed us to quantify an upper bound on the mean anatomical distortion accordingly. We used two resting state datasets, one recorded with head-casts and one without. In the head-cast data, the Empirical Bayesian Beamformer (EBB) algorithm showed the best mean anatomical discrimination (3.7 mm) compared with Minimum Norm/LORETA (6.0 mm) and Multiple Sparse Priors (9.4 mm). This pattern was replicated in the second (conventional dataset) although with a marginally poorer (non-significant) prediction of the missing (cross-validated) data. Our findings suggest that the abundant resting state data now commonly available could be used to refine and validate MEG source reconstruction methods and/or recording paradigms. Highlights • Resting state data provides an unbiased dataset for comparing MEG source estimates. • We use a hidden Markov model to break the data into stationary segments. • We reconstruct these data onto a range of distorted cortical surfaces. • The sensitivity of any algorithm to spatial distortion is quantified in millimetres. [ABSTRACT FROM AUTHOR]

Abstract The spatial topology of the human motor cortex has been well studied, particularly using functional Magnetic Resonance Imaging (fMRI) which allows spatial separation of haemodynamic responses arising from stimulation of different body parts, individual digits and even spatially separate areas of the same digit. However, the spatial organisation of electrophysiological responses, particularly neural oscillations (rhythmic changes in electrical potential across cellular assemblies) has been less well studied. Mapping the spatial signature of neural oscillations is possible using magnetoencephalography (MEG), however spatial differentiation of responses induced by movement of separate digits is a challenge, because the brain regions involved are separated by only a few millimetres. In this paper we first show, in simulation, how to optimise experimental design and beamformer spatial filtering techniques to increase the spatial specificity of MEG derived functional images. Combining this result with experimental data, we then capture the organisation of the post-movement beta band (13–30 Hz) oscillatory response to movement of digits 2 and 5 of the dominant hand, in individual subjects. By comparing these MEG results to ultra-high field (7T) fMRI, we also show significant spatial agreement between beta modulation and the blood oxygenation level dependent (BOLD) response. Our results show that, when using an optimised inverse solution and controlling subject movement (using custom fitted foam padding) the spatial resolution of MEG can be of order 3–5 mm. The method described offers exciting potential to understand better the cortical organisation of oscillations, and to probe such organisation in patient populations where those oscillations are known to be abnormal. Highlights • Aim is to map the topological organisation of neural oscillations in motor cortex. • MEG spatial resolution optimised by temporal separation of sources. • Subject motion controlled using foam headcasts. • Cortical representation of Digit 2 and Digit 5 separated spatially. • Post movement beta rebound maps motortopically in agreement with BOLD responses. [ABSTRACT FROM AUTHOR]

Abstract The discovery of hemodynamic (BOLD-fMRI) resting-state networks (RSNs) has brought about a fundamental shift in our thinking about the role of intrinsic brain activity. The electrophysiological underpinnings of RSNs remain largely elusive and it has been shown only recently that electric cortical rhythms are organized into the same RSNs as hemodynamic signals. Most electrophysiological studies into RSNs use magnetoencephalography (MEG) or scalp electroencephalography (EEG), which limits the spatial resolution with which electrophysiological RSNs can be observed. Due to their close proximity to the cortical surface, electrocorticographic (ECoG) recordings can potentially provide a more detailed picture of the functional organization of resting-state cortical rhythms, albeit at the expense of spatial coverage. In this study we propose using source-space spatial independent component analysis (spatial ICA) for identifying generators of resting-state cortical rhythms as recorded with ECoG and for reconstructing their functional connectivity. Network structure is assessed by two kinds of connectivity measures: instantaneous correlations between band-limited amplitude envelopes and oscillatory phase-locking. By simulating rhythmic cortical generators, we find that the reconstruction of oscillatory phase-locking is more challenging than that of amplitude correlations, particularly for low signal-to-noise levels. Specifically, phase-lags can both be over- and underestimated, which troubles the interpretation of lag-based connectivity measures. We illustrate the methodology on somatosensory beta rhythms recorded from a macaque monkey using ECoG. The methodology decomposes the resting-state sensorimotor network into three cortical generators, distributed across primary somatosensory and primary and higher-order motor areas. The generators display significant and reproducible amplitude correlations and phase-locking values with non-zero lags. Our findings illustrate the level of spatial detail attainable with source-projected ECoG and motivates wider use of the methodology for studying resting-state as well as event-related cortical dynamics in macaque and human. • Reconstruction of source-space resting-state networks from ECoG sensor-data is feasible. • Source-modeling of ECoG data yields higher spatial resolution than scalp EEG and MEG. • Sensorimotor beta oscillations comprise a distributed network of motor and somatosensory generators. • Somatosensory generator has internal dynamic structure and behaves as a propagating wave. [ABSTRACT FROM AUTHOR]

Abstract For several years it has been argued that neural synchronisation is crucial for cognition. The idea that synchronised temporal patterns between different neural groups carries information above and beyond the isolated activity of these groups has inspired a shift in focus in the field of functional neuroimaging. Specifically, investigation into the activation elicited within certain regions by some stimulus or task has, in part, given way to analysis of patterns of co-activation or functional connectivity between distal regions. Recently, the functional connectivity community has been looking beyond the assumptions of stationarity that earlier work was based on, and has introduced methods to incorporate temporal dynamics into the analysis of connectivity. In particular, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG/EEG)), which provides direct measurement of whole-brain activity and rich temporal information, offers an exceptional window into such (potentially fast) brain dynamics. In this review, we discuss challenges, solutions, and a collection of analysis tools that have been developed in recent years to facilitate the investigation of dynamic functional connectivity using these imaging modalities. Further, we discuss the applications of these approaches in the study of cognition and neuropsychiatric disorders. Finally, we review some existing developments that, by using realistic computational models, pursue a deeper understanding of the underlying causes of non-stationary connectivity. Highlights • Functional connectivity studies have recently started investigating the dynamics of functional networks. • Electrophysiological measures provide a means to probe the fastest dynamics • We review the methodology to assess connectivity on time scales of a few seconds to milliseconds and their applications. • We also look at the computational models being developed to explain how connectivity manifests. [ABSTRACT FROM AUTHOR]

Abstract The application of machine learning methods to neuroimaging data has fundamentally altered the field of cognitive neuroscience. Future progress in understanding brain function using these methods will require addressing a number of key methodological and interpretive challenges. Because these challenges often remain unseen and metaphorically “haunt” our efforts to use these methods to understand the brain, we refer to them as “ghosts”. In this paper, we describe three such ghosts, situate them within a more general framework from philosophy of science, and then describe steps to address them. The first ghost arises from difficulties in determining what information machine learning classifiers use for decoding. The second ghost arises from the interplay of experimental design and the structure of information in the brain – that is, our methods embody implicit assumptions about information processing in the brain, and it is often difficult to determine if those assumptions are satisfied. The third ghost emerges from our limited ability to distinguish information that is merely decodable from the brain from information that is represented and used by the brain. Each of the three ghosts place limits on the interpretability of decoding research in cognitive neuroscience. There are no easy solutions, but facing these issues squarely will provide a clearer path to understanding the nature of representation and computation in the human brain. Highlights • Provides a philosophical framework for thinking about applications of machine learning to cognitive neuroscience datasets. • Discussion of current challenges for neural decoding research. • Gives suggestions about how to address contemporary challenges in decoding research. [ABSTRACT FROM AUTHOR]