GLM-beamformer method demonstrates stationary field, alpha ERD and gamma ERS co-localisation with fMRI BOLD response in visual cortex

Detection of Language Lateralization Using Spectral Analysis of EEG

PURPOSE

Language lateralization relies on expensive equipment and can be difficult to tolerate. We assessed if lateralized brain responses to a language task can be detected with spectral analysis of electroencephalography (EEG).

METHODS

Twenty right-handed, neurotypical adults (28 ± 10 years; five males) performed a verb generation task and two control tasks (word listening and repetition). We measured changes in EEG activity elicited by tasks (the event-related spectral perturbation [ERSP]) in the theta, alpha, beta, and gamma frequency bands in two language (superior temporal and inferior frontal [ST and IF]) and one control (occipital [Occ]) region bilaterally. We tested whether language tasks elicited (1) changes in spectral power from baseline (significant ERSP) at any region or (2) asymmetric ERSPs between matched left and right regions.

RESULTS

Left IF beta power (-0.37±0.53, t = -3.12, P = 0.006) and gamma power in all regions decreased during verb generation. Asymmetric ERSPs (right > left) occurred between the (1) IF regions in the beta band (right vs. left difference of 0.23±0.37, t(19) = -2.80, P = 0.0114) and (2) ST regions in the alpha band (right vs. left difference of 0.48±0.63, t(19) = -3.36, P = 0.003). No changes from baseline or hemispheric asymmetries were noted in language regions during control tasks. On the individual level, 16 (80%) participants showed decreased left IF beta power from baseline, and 16 showed ST alpha asymmetry. Eighteen participants (90%) showed one of these two findings.

CONCLUSIONS

Spectral EEG analysis detects lateralized responses during language tasks in frontal and temporal regions. Spectral EEG analysis could be developed into a readily available language lateralization modality.

Brain Dynamics of Spatial Reference Frame Proclivity in Active Navigation

Recent research into navigation strategy of different spatial reference frames (self-centered egocentric reference frame and environment-centered allocentric reference frame) has revealed that the parietal cortex plays an important role in processing allocentric information to provide a translation function between egocentric and allocentric spatial reference frames. However, most studies merely focused on a passive experimental environment, which is not truly representative of our daily spatial learning/navigation tasks. This study investigated the factor associated with brain dynamics that causes people to switch their preferred spatial strategy in both active and passive navigations to bridge the gap. Virtual reality (VR) technique and Omni treadmill are applied to realize actively walking for active navigation, and for passive navigation, participants were sitting while conducting the same task. Electroencephalography (EEG) signals were recorded to monitor spectral perturbations on transitions between egocentric and allocentric frames during a path integration task. Forty-one right-handed male participants from authors' university participated this study. Our brain dynamics results showed navigation involved areas including the parietal cortex with modulation in the alpha band, the occipital cortex with beta and low gamma band perturbations, and the frontal cortex with theta perturbation. Differences were found between two different turning-angle paths in the alpha band in parietal cluster event-related spectral perturbations (ERSPs). In small turning-angle paths, allocentric participants showed stronger alpha desynchronization than egocentric participants; in large turning-angle paths, participants for two reference frames had a smaller difference in the alpha frequency band. Behavior results of homing errors also corresponded to brain dynamic results, indicating that a larger angle path caused the allocentric to have a higher tendency to become egocentric navigators in the active navigation environment.

FMRI and intra-cranial electrocorticography recordings in the same human subjects reveals negative BOLD signal coupled with silenced neuronal activity

Positive blood oxygenation level-dependent (BOLD) responses (PBR), as measured by functional Magnetic Resonance Imaging (fMRI), are the most utilized measurements to non-invasively map activity in the brain. Recent studies have consistently shown that BOLD responses are not exclusively positive. Negative BOLD responses (NBR) have been reported in response to specific sensory stimulations and tasks. However, the exact relationship between NBR and the underlying metabolic and neuronal demand is still under debate. In this study, we investigated the neurophysiological basis of negative BOLD using fMRI and intra-cranial electrophysiology (electrocorticography, ECoG) measurements from the same human participants. We show that, for those electrodes that responded to visual stimulation, PBR are correlated with high-frequency band (HFB) responses. Crucially, NBR were associated with an absence of HFB power responses and an unpredicted decrease in the alpha power responses.

Data-driven beamforming technique to attenuate ballistocardiogram artefacts in electroencephalography-functional magnetic resonance imaging without detecting cardiac pulses in electrocardiography recordings

Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a very promising non‐invasive neuroimaging technique. However, EEG data obtained from the simultaneous EEG–fMRI are strongly influenced by MRI‐related artefacts, namely gradient artefacts (GA) and ballistocardiogram (BCG) artefacts. When compared to the GA correction, the BCG correction is more challenging to remove due to its inherent variabilities and dynamic changes over time. The standard BCG correction (i.e., average artefact subtraction [AAS]), require detecting cardiac pulses from simultaneous electrocardiography (ECG) recording. However, ECG signals are also distorted and will become problematic for detecting reliable cardiac peaks. In this study, we focused on a beamforming spatial filtering technique to attenuate all unwanted source activities outside of the brain. Specifically, we applied the beamforming technique to attenuate the BCG artefact in EEG–fMRI, and also to recover meaningful task‐based neural signals during an attentional network task (ANT) which required participants to identify visual cues and respond accurately. We analysed EEG–fMRI data in 20 healthy participants during the ANT, and compared four different BCG corrections (non‐BCG corrected, AAS BCG corrected, beamforming + AAS BCG corrected, beamforming BCG corrected). We demonstrated that the beamforming approach did not only significantly reduce the BCG artefacts, but also significantly recovered the expected task‐based brain activity when compared to the standard AAS correction. This data‐driven beamforming technique appears promising especially for longer data acquisition of sleep and resting EEG–fMRI. Our findings extend previous work regarding the recovery of meaningful EEG signals by an optimized suppression of MRI‐related artefacts.

Abnormalities in cortical pattern of coherence in migraine detected using ultra high-density EEG

Individuals with migraine generally experience photophobia and/or phonophobia during and between migraine attacks. Many different mechanisms have been postulated to explain these migraine phenomena including abnormal patterns of connectivity across the cortex. The results, however, remain contradictory and there is no clear consensus on the nature of the cortical abnormalities in migraine. Here, we uncover alterations in cortical patterns of coherence (connectivity) in interictal migraineurs during the presentation of visual and auditory stimuli and during rest. We used a high-density EEG system, with 128 customized electrode locations, to compare inter- and intra-hemispheric coherence in the interictal period from 17 individuals with migraine (12 female) and 18 age- and gender-matched healthy control subjects. During presentations of visual (vertical grating pattern) and auditory (modulated tone) stimulation which varied in temporal frequency (4 and 6 Hz), and during rest, participants performed a colour detection task at fixation. Analyses included characterizing the inter- and intra-hemisphere coherence between the scalp EEG channels over 2-s time intervals and over different frequency bands at different spatial distances and spatial clusters. Pearson's correlation coefficients were estimated at zero-lag. Repeated measures analyses-of-variance revealed that, relative to controls, migraineurs exhibited significantly (i) faster colour detection performance, (ii) lower spatial coherence of alpha-band activity, for both inter- and intra-hemisphere connections, and (iii) the reduced coherence occurred predominantly in frontal clusters during both sensory conditions, regardless of the stimulation frequency, as well as during the resting-state. The abnormal patterns of EEG coherence in interictal migraineurs during visual and auditory stimuli, as well as at rest (eyes open), may be associated with the cortical hyper-responsivity that is characteristic of abnormal sensory processing in migraineurs.

α-tACS over the somatosensory cortex enhances tactile spatial discrimination in healthy subjects with low alpha activity

INTRODUCTION

Spontaneous oscillations in the somatosensory cortex, especially of the alpha (8 - 14 Hz) and gamma (60 - 80 Hz) frequencies, affect tactile perception; moreover, these oscillations can be selectively modulated by frequency-matched transcranial alternating current stimulation (tACS) on the basis of ongoing oscillatory brain activity. To examine whether tACS can actually improve tactile perception via alpha and gamma modulation, we measured the effects of 10-Hz and 70-Hz tACS (α- and γ-tACS) on the left somatosensory cortex on right-finger tactile spatial orientation discrimination, and the associations between performance changes and individual alpha and gamma activities.

METHODS

Fifteen neurologically healthy subjects were recruited into this study. Electroencephalography (EEG) was performed before the first day, to assess the normal alpha- and gamma-activity levels. A grating orientation discrimination task was performed before and during 10-Hz and 70-Hz tACS.

RESULTS

The 10-Hz tACS protocol decreased the grating orientation discrimination threshold, primarily in subjects with low alpha event-related synchronization (ERS). In contrast, the 70-Hz tACS had no effect on the grating orientation discrimination threshold.

CONCLUSIONS

This study showed that 10-Hz tACS can improve tactile orientation discrimination in subjects with low alpha activity. Alpha-frequency tACS may help identify the contributions of these oscillations to other neurophysiological and pathological processes.

Mapping the topological organisation of beta oscillations in motor cortex using MEG

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.

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Aim is to map the topological organisation of neural oscillations in motor cortex.

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MEG spatial resolution optimised by temporal separation of sources.

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Subject motion controlled using foam headcasts.

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Cortical representation of Digit 2 and Digit 5 separated spatially.

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Post movement beta rebound maps motortopically in agreement with BOLD responses.

Non-invasive Investigation of Human Hippocampal Rhythms Using Magnetoencephalography: A Review

Hippocampal rhythms are believed to support crucial cognitive processes including memory, navigation, and language. Due to the location of the hippocampus deep in the brain, studying hippocampal rhythms using non-invasive magnetoencephalography (MEG) recordings has generally been assumed to be methodologically challenging. However, with the advent of whole-head MEG systems in the 1990s and development of advanced source localization techniques, simulation and empirical studies have provided evidence that human hippocampal signals can be sensed by MEG and reliably reconstructed by source localization algorithms. This paper systematically reviews simulation studies and empirical evidence of the current capacities and limitations of MEG “deep source imaging” of the human hippocampus. Overall, these studies confirm that MEG provides a unique avenue to investigate human hippocampal rhythms in cognition, and can bridge the gap between animal studies and human hippocampal research, as well as elucidate the functional role and the behavioral correlates of human hippocampal oscillations.

Abnormal language-related oscillatory responses in primary progressive aphasia

Patients with Primary Progressive Aphasia (PPA) may react to linguistic stimuli differently than healthy controls, reflecting degeneration of language networks and engagement of compensatory mechanisms. We used magnetoencephalography (MEG) to evaluate oscillatory neural responses in sentence comprehension, in patients with PPA and age-matched controls. Participants viewed sentences containing semantically and syntactically anomalous words that evoke distinct oscillatory responses. For age-matched controls, semantic anomalies elicited left-lateralized 8–30 Hz power decreases distributed along ventral brain regions, whereas syntactic anomalies elicited bilateral power decreases in both ventral and dorsal regions. In comparison to controls, patients with PPA showed altered patterns of induced oscillations, characterized by delayed latencies and attenuated amplitude, which were correlated with linguistic impairment measured offline. The recruitment of right hemisphere temporo-parietal areas (also found in controls) was correlated with preserved semantic processing abilities, indicating that preserved neural activity in these regions was able to support successful semantic processing. In contrast, syntactic processing was more consistently impaired in PPA, regardless of neural activity patterns, suggesting that this domain of language is particularly vulnerable to the neuronal loss. In addition, we found that delayed peak latencies of oscillatory responses were associated with lower accuracy for detecting semantic anomalies, suggesting that language deficits observed in PPA may be linked to delayed or slowed information processing.

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Evaluated induced oscillations in patients with PPA using MEG.

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PPA patients showed delayed latencies and attenuated amplitude of responses.

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Preserved right hemisphere regions support semantic processing.

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Delayed latencies of oscillatory responses associated with impaired performance.

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Language deficits in PPA linked to delayed or slowed information processing.

Electrophysiological impact of multiple concussions in asymptomatic athletes: A re-analysis based on alpha activity during a visual-spatial attention task

Most EEG studies used event-related potentials to assess long-term and cumulative effects of sport-related concussions on brain activity. Time-frequency methods provide another approach that allows the detection of subtle shifts in types and patterns of brain oscillations. We sought to discover whether event-related alpha activity would be significantly affected in asymptomatic multi-concussed athletes. We measured the amplitude of alpha activity (8-12Hz) from the EEG recorded during a visual-spatial attention task to compare event-related alpha perturbations in 13 multi-concussed athletes and 14 age-equivalent, non-concussed teammates. Relative to non-concussed athletes, multi-concussed athletes showed significantly less event-related perturbations time-locked to stimulus presentation. Alpha activity alterations were closely related to the number of concussions sustained. Event-related alpha activity differed in asymptomatic multi-concussed athletes when compared to controls. Our study suggests that low-level neurophysiological underpinnings of the deployment of visual-spatial attention are affected in multi-concussed athletes even though their last concussion occurred on average 30 months prior to testing.

The Cumulative Effects of Predictability on Synaptic Gain in the Auditory Processing Stream

Stimulus predictability can lead to substantial modulations of brain activity, such as shifts in sustained magnetic field amplitude, measured with magnetoencephalography (MEG). Here, we provide a mechanistic explanation of these effects using MEG data acquired from healthy human volunteers (N = 13, 7 female). In a source-level analysis of induced responses, we established the effects of orthogonal predictability manipulations of rapid tone-pip sequences (namely, sequence regularity and alphabet size) along the auditory processing stream. In auditory cortex, regular sequences with smaller alphabets induced greater gamma activity. Furthermore, sequence regularity shifted induced activity in frontal regions toward higher frequencies. To model these effects in terms of the underlying neurophysiology, we used dynamic causal modeling for cross-spectral density and estimated slow fluctuations in neural (postsynaptic) gain. Using the model-based parameters, we accurately explain the sensor-level sustained field amplitude, demonstrating that slow changes in synaptic efficacy, combined with sustained sensory input, can result in profound and sustained effects on neural responses to predictable sensory streams.

SIGNIFICANCE STATEMENT Brain activity can be strongly modulated by the predictability of stimuli it is currently processing. An example of such a modulation is a shift in sustained magnetic field amplitude, measured with magnetoencephalography. Here, we provide a mechanistic explanation of these effects. First, we establish the oscillatory neural correlates of independent predictability manipulations in hierarchically distinct areas of the auditory processing stream. Next, we use a biophysically realistic computational model to explain these effects in terms of the underlying neurophysiology. Finally, using the model-based parameters describing neural gain modulation, we can explain the previously unexplained effects observed at the sensor level. This demonstrates that slow modulations of synaptic gain can result in profound and sustained effects on neural activity.

Heightened amygdala responsiveness in s-carriers of 5-HTTLPR genetic polymorphism reflects enhanced cortical rather than subcortical inputs: An MEG study

Short allele carriers (S-carriers) of the serotonin transporter gene (5-HTTLPR) show an elevated amygdala response to emotional stimuli relative to long allele carriers (LL-homozygous). However, whether this reflects increased responsiveness of the amygdala generally or interactions between the amygdala and the specific input systems remains unknown. It is argued that the amygdala receives input via a quick subcortical and a slower cortical pathway. If the elevated amygdala response in S-carriers reflects generally increased amygdala responding, then group differences in amygdala should be seen across the amygdala response time course. However, if the difference is a secondary consequence of enhanced amygdala-cortical interactions, then group differences might only be present later in the amygdala response. Using magnetoencephalography (MEG), we found an enhanced amygdala response to fearful expressions starting 40-50 ms poststimulus. However, group differences in the amygdala were only seen 190-200 ms poststimulus, preceded by increased superior temporal sulcus (STS) responses in S-carriers from 130 to 140 ms poststimulus. An enhanced amygdala response to angry expressions started 260-270 ms poststimulus with group differences in the amygdala starting at 160-170 ms poststimulus onset, preceded by increased STS responses in S-carriers from 150 to 160 ms poststimulus. These suggest that enhanced amygdala responses in S-carriers might reflect enhanced STS-amygdala connectivity in S-carriers. Hum Brain Mapp 38:4313-4321, 2017. © 2017 Wiley Periodicals, Inc.

Correspondence between fMRI and electrophysiology during visual motion processing in human MT

Changes in brain neuronal activity are reflected by hemodynamic responses mapped through Blood Oxygenation Level Dependent (BOLD) functional magnetic resonance imaging (fMRI), a primary tool to measure brain functioning non-invasively. However, the exact relationship between hemodynamics and neuronal function is still a matter of debate. Here, we combine 3T BOLD fMRI and High Frequency Band (HFB) electrocorticography (ECoG) signals to investigate the relationship between neuronal activity and hemodynamic responses in the human Middle Temporal complex (hMT+), a higher order brain area involved in visual motion processing. We modulated the ECoG HFB and fMRI BOLD responses with a visual stimulus moving at different temporal frequencies, and compared measured BOLD responses to estimated BOLD responses that were predicted from the temporal profile of the HFB power change. We show that BOLD responses under an electrode over hMT+ can be well predicted not only be the strength of the neuronal response but also by the temporal profile of the HFB responses recorded by this electrode. Our results point to a linear relationship between BOLD and neuronal activity in hMT+, extending previous findings on primary cortex to higher order cortex.

Abnormal task driven neural oscillations in multiple sclerosis: A visuomotor MEG study

Multiple sclerosis (MS) is a debilitating disease commonly attributed to degradation of white matter myelin. Symptoms include fatigue, as well as problems associated with vision and movement. Although areas of demyelination in white matter are observed routinely in patients undergoing MRI scans, such measures are often a poor predictor of disease severity. For this reason, it is instructive to measure associated changes in brain function. Widespread white‐matter demyelination may lead to delays of propagation of neuronal activity, and with its excellent temporal resolution, magnetoencephalography can be used to probe such delays in controlled conditions (e.g., during a task). In healthy subjects, responses to visuomotor tasks are well documented: in motor cortex, movement elicits a localised decrease in the power of beta band oscillations (event‐related beta desynchronisation) followed by an increase above baseline on movement cessation (post‐movement beta rebound (PMBR)). In visual cortex, visual stimulation generates increased gamma oscillations. In this study, we use a visuomotor paradigm to measure these responses in MS patients and compare them to age‐ and gender‐matched healthy controls. We show a significant increase in the time‐to‐peak of the PMBR in patients which correlates significantly with the symbol digit modalities test: a measure of information processing speed. A significant decrease in the amplitude of visual gamma oscillations in patients is also seen. These findings highlight the potential value of electrophysiological imaging in generating a new understanding of visual disturbances and abnormal motor control in MS patients. Hum Brain Mapp 38:2441–2453, 2017. © 2017 Wiley Periodicals, Inc.

Multimodal Classification of Schizophrenia Patients with MEG and fMRI Data Using Static and Dynamic Connectivity Measures

Mental disorders like schizophrenia are currently diagnosed by physicians/psychiatrists through clinical assessment and their evaluation of patient's self-reported experiences as the illness emerges. There is great interest in identifying biological markers of prognosis at the onset of illness, rather than relying on the evolution of symptoms across time. Functional network connectivity, which indicates a subject's overall level of "synchronicity" of activity between brain regions, demonstrates promise in providing individual subject predictive power. Many previous studies reported functional connectivity changes during resting-state using only functional magnetic resonance imaging (fMRI). Nevertheless, exclusive reliance on fMRI to generate such networks may limit the inference of the underlying dysfunctional connectivity, which is hypothesized to be a factor in patient symptoms, as fMRI measures connectivity via hemodynamics. Therefore, combination of connectivity assessments using fMRI and magnetoencephalography (MEG), which more directly measures neuronal activity, may provide improved classification of schizophrenia than either modality alone. Moreover, recent evidence indicates that metrics of dynamic connectivity may also be critical for understanding pathology in schizophrenia. In this work, we propose a new framework for extraction of important disease related features and classification of patients with schizophrenia based on using both fMRI and MEG to investigate functional network components in the resting state. Results of this study show that the integration of fMRI and MEG provides important information that captures fundamental characteristics of functional network connectivity in schizophrenia and is helpful for prediction of schizophrenia patient group membership. Combined fMRI/MEG methods, using static functional network connectivity analyses, improved classification accuracy relative to use of fMRI or MEG methods alone (by 15 and 12.45%, respectively), while combined fMRI/MEG methods using dynamic functional network connectivity analyses improved classification up to 5.12% relative to use of fMRI alone and up to 17.21% relative to use of MEG alone.

Magnetoencephalographic and functional MRI connectomics in schizophrenia via intra- and inter-network connectivity

Examination of intrinsic functional connectivity using functional MRI (fMRI) has provided important findings regarding dysconnectivity in schizophrenia. Extending these results using a complementary neuroimaging modality, magnetoencephalography (MEG), we present the first direct comparison of functional connectivity between schizophrenia patients and controls, using these two modalities combined. We developed a novel MEG approach for estimation of networks using MEG that incorporates spatial independent component analysis (ICA) and pairwise correlations between independent component timecourses, to estimate intra- and intern-network connectivity. This analysis enables group-level inference and testing of between-group differences. Resting state MEG and fMRI data were acquired from a large sample of healthy controls (n=45) and schizophrenia patients (n=46). Group spatial ICA was performed on fMRI and MEG data to extract intrinsic fMRI and MEG networks and to compensate for signal leakage in MEG. Similar, but not identical spatial independent components were detected for MEG and fMRI. Analysis of functional network connectivity (FNC; i.e., pairwise correlations in network (ICA component) timecourses) revealed a differential between-modalities pattern, with greater connectivity among occipital networks in fMRI and among frontal networks in MEG. Most importantly, significant differences between controls and patients were observed in both modalities. MEG FNC results in particular indicated dysfunctional hyperconnectivity within frontal and temporal networks in patients, while in fMRI FNC was always greater for controls than for patients. This is the first study to apply group spatial ICA as an approach to leakage correction, and as such our results may be biased by spatial leakage effects. Results suggest that combining these two neuroimaging modalities reveals additional disease-relevant patterns of connectivity that were not detectable with fMRI or MEG alone.

Oscillatory brain activity during multisensory attention reflects activation, disinhibition, and cognitive control

In this study, we used a novel multisensory attention paradigm to investigate attention-modulated cortical oscillations over a wide range of frequencies using magnetencephalography in healthy human participants. By employing a task that required the evaluation of the congruence of audio-visual stimuli, we promoted the formation of widespread cortical networks including early sensory cortices as well as regions associated with cognitive control. We found that attention led to increased high-frequency gamma-band activity and decreased lower frequency theta-, alpha-, and beta-band activity in early sensory cortex areas. Moreover, alpha-band coherence decreased in visual cortex. Frontal cortex was found to exert attentional control through increased low-frequency phase synchronisation. Crossmodal congruence modulated beta-band coherence in mid-cingulate and superior temporal cortex. Together, these results offer an integrative view on the concurrence of oscillations at different frequencies during multisensory attention.

Quiet connections: Reduced fronto-temporal connectivity in nondemented Parkinson's Disease during working memory encoding

Parkinson's disease (PD) is a common neurodegenerative disorder characterized primarily by motor symptoms such as bradykinesia, muscle rigidity, and resting tremor. It is now broadly accepted that these motor symptoms frequently co-occur with cognitive impairments, with deficits in working memory and attention being among the most common cognitive sequelae associated with PD. While these cognitive impairments are now recognized, the underlying neural dynamics and precise regions involved remain largely unknown. To this end, we examined the oscillatory dynamics and interregional functional connectivity that serve working memory processing in a group of unmedicated adults with PD and a matched group without PD. Each participant completed a high-load, Sternberg-type working memory task during magnetoencephalography (MEG), and we focused on the encoding and maintenance phases. All data were transformed into the time-frequency domain and significant oscillatory activity was imaged using a beamforming approach. Phase-coherence (connectivity) was also computed among the brain subregions exhibiting the strongest responses. Our most important findings were that unmedicated patients with PD had significantly diminished working memory performance (i.e., accuracy), and reduced functional connectivity between left inferior frontal cortices and left supramarginal-superior temporal cortices compared to participants without PD during the encoding phase of working memory processing. We conclude that patients with PD have reduced neural interactions between left prefrontal executive circuits and temporary verbal storage centers in the left supramarginal/superior temporal cortices during the stimulus encoding phase, which may underlie their diminished working memory function. Hum Brain Mapp 37:3224-3235, 2016. © 2016 Wiley Periodicals, Inc.

A multi-layer network approach to MEG connectivity analysis

Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiological signal makes gaining a complete picture challenging. Specifically, connectivity can be calculated as statistical interdependencies between neural oscillations within a large range of different frequency bands. Further, connectivity can be computed between frequency bands. This pan-spectral network hierarchy likely helps to mediate simultaneous formation of multiple brain networks, which support ongoing task demand. However, to date it has been largely overlooked, with many electrophysiological functional connectivity studies treating individual frequency bands in isolation. Here, we combine oscillatory envelope based functional connectivity metrics with a multi-layer network framework in order to derive a more complete picture of connectivity within and between frequencies. We test this methodology using MEG data recorded during a visuomotor task, highlighting simultaneous and transient formation of motor networks in the beta band, visual networks in the gamma band and a beta to gamma interaction. Having tested our method, we use it to demonstrate differences in occipital alpha band connectivity in patients with schizophrenia compared to healthy controls. We further show that these connectivity differences are predictive of the severity of persistent symptoms of the disease, highlighting their clinical relevance. Our findings demonstrate the unique potential of MEG to characterise neural network formation and dissolution. Further, we add weight to the argument that dysconnectivity is a core feature of the neuropathology underlying schizophrenia.

Brain-computer interfaces for patients with disorders of consciousness

The disorders of consciousness refer to clinical conditions that follow a severe head injury. Patients diagnosed as in a vegetative state lack awareness, while patients diagnosed as in a minimally conscious state retain fluctuating awareness. However, it is a challenge to accurately diagnose these disorders with clinical assessments of behavior. To improve diagnostic accuracy, neuroimaging-based approaches have been developed to detect the presence or absence of awareness in patients who lack overt responsiveness. For the small subset of patients who retain awareness, brain-computer interfaces could serve as tools for communication and environmental control. Here we review the existing literature concerning the sensory and cognitive abilities of patients with disorders of consciousness with respect to existing brain-computer interface designs. We highlight the challenges of device development for this special population and address some of the most promising approaches for future investigations.

Functional reorganization of language networks for semantics and syntax in chronic stroke: Evidence from MEG

Using magnetoencephalography, we investigated the potential of perilesional and contralesional activity to support language recovery in patients with poststroke aphasia. In healthy young controls, left-lateralized ventral frontotemporal regions responded to semantic anomalies during sentence comprehension and bilateral dorsal frontoparietal regions responded to syntactic anomalies. Older adults showed more extensive bilateral responses to the syntactic anomalies and less lateralized responses to the semantic anomalies, with decreased activation in the left occipital and parietal regions for both semantic and syntactic anomalies. In aphasic participants, we observed compensatory recruitment in the right hemisphere (RH), which varied depending on the type of linguistic information that was processed. For semantic anomalies, aphasic patients activated some preserved left hemisphere regions adjacent to the lesion, as well as homologous parietal and temporal RH areas. Patients also recruited right inferior and dorsolateral frontal cortex that was not activated in the healthy participants. Responses for syntactic anomalies did not reach significance in patients. Correlation analyses indicated that recruitment of homologous temporoparietal RH areas is associated with better semantic performance, whereas higher accuracy on the syntactic task was related to bilateral superior temporoparietal and right frontal activity. The results suggest that better recovery of semantic processing is associated with a shift to ventral brain regions in the RH. In contrast, preservation of syntactic processing is mediated by dorsal areas, bilaterally, although recovery of syntactic processing tends to be poorer than semantic. Hum Brain Mapp 37:2869-2893, 2016. © 2016 Wiley Periodicals, Inc.

Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions

Understanding the electrophysiological basis of resting state networks (RSNs) in the human brain is a critical step towards elucidating how inter-areal connectivity supports healthy brain function. In recent years, the relationship between RSNs (typically measured using haemodynamic signals) and electrophysiology has been explored using functional Magnetic Resonance Imaging (fMRI) and magnetoencephalography (MEG). Significant progress has been made, with similar spatial structure observable in both modalities. However, there is a pressing need to understand this relationship beyond simple visual similarity of RSN patterns. Here, we introduce a mathematical model to predict fMRI-based RSNs using MEG. Our unique model, based upon a multivariate Taylor series, incorporates both phase and amplitude based MEG connectivity metrics, as well as linear and non-linear interactions within and between neural oscillations measured in multiple frequency bands. We show that including non-linear interactions, multiple frequency bands and cross-frequency terms significantly improves fMRI network prediction. This shows that fMRI connectivity is not only the result of direct electrophysiological connections, but is also driven by the overlap of connectivity profiles between separate regions. Our results indicate that a complete understanding of the electrophysiological basis of RSNs goes beyond simple frequency-specific analysis, and further exploration of non-linear and cross-frequency interactions will shed new light on distributed network connectivity, and its perturbation in pathology.

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We introduce a mathematical model to predict fMRI-based RSNs using MEG.

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Our model is based on a multi-variate Taylor series expansion.

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The electrophysiological basis of RSNs goes beyond frequency-band specific analysis.

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RSNs result 1) from multiple frequency bands and cross-frequency coupling.

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RSNs result 2) from direct and shared electrophysiological connectivity.

Auditory cortical deactivation during speech production and following speech perception: an EEG investigation of the temporal dynamics of the auditory alpha rhythm

Sensorimotor integration (SMI) across the dorsal stream enables online monitoring of speech. Jenson et al. (2014) used independent component analysis (ICA) and event related spectral perturbation (ERSP) analysis of electroencephalography (EEG) data to describe anterior sensorimotor (e.g., premotor cortex, PMC) activity during speech perception and production. The purpose of the current study was to identify and temporally map neural activity from posterior (i.e., auditory) regions of the dorsal stream in the same tasks. Perception tasks required "active" discrimination of syllable pairs (/ba/ and /da/) in quiet and noisy conditions. Production conditions required overt production of syllable pairs and nouns. ICA performed on concatenated raw 68 channel EEG data from all tasks identified bilateral "auditory" alpha (α) components in 15 of 29 participants localized to pSTG (left) and pMTG (right). ERSP analyses were performed to reveal fluctuations in the spectral power of the α rhythm clusters across time. Production conditions were characterized by significant α event related synchronization (ERS; pFDR < 0.05) concurrent with EMG activity from speech production, consistent with speech-induced auditory inhibition. Discrimination conditions were also characterized by α ERS following stimulus offset. Auditory α ERS in all conditions temporally aligned with PMC activity reported in Jenson et al. (2014). These findings are indicative of speech-induced suppression of auditory regions, possibly via efference copy. The presence of the same pattern following stimulus offset in discrimination conditions suggests that sensorimotor contributions following speech perception reflect covert replay, and that covert replay provides one source of the motor activity previously observed in some speech perception tasks. To our knowledge, this is the first time that inhibition of auditory regions by speech has been observed in real-time with the ICA/ERSP technique.

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

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

The Faces of Predictive Coding

Recent neurophysiological accounts of predictive coding hypothesized that a mismatch of prediction and sensory evidence-a prediction error (PE)-should be signaled by increased gamma-band activity (GBA) in the cortical area where prediction and evidence are compared. This hypothesis contrasts with alternative accounts where violated predictions should lead to reduced neural responses. We tested these hypotheses by violating predictions about face orientation and illumination direction in a Mooney face-detection task, while recording magnetoencephalographic responses in a large sample of 48 human subjects. The investigated predictions, acquired via lifelong experience, are known to be processed at different time points and brain regions during face recognition.Behavioral responses confirmed the induction of PEs by our task. Beamformer source analysis revealed an early PE signal for unexpected orientation in visual brain areas followed by a PE signal for unexpected illumination in areas involved in 3D shape from shading and spatial working memory. Both PE signals were reflected by increases in high-frequency (68-140 Hz) GBA. In high-frequency GBA we also observed a late interaction effect in visual brain areas, probably corresponding to a high-level PE signal. In addition, increased high-frequency GBA for expected illumination was observed in brain areas involved in attention to internal representations. Our results strongly support the hypothesis that increased GBA signals PEs. Additionally, GBA may represent attentional effects.

Dynamic causal modelling of EEG and fMRI to characterize network architectures in a simple motor task

Dynamic causal modelling (DCM) has extended the understanding of brain network dynamics in a variety of functional systems. In the motor system, DCM studies based on functional magnetic resonance imaging (fMRI) or on magneto-/electroencephalography (M/EEG) have demonstrated movement-related causal information flow from secondary to primary motor areas and have provided evidence for nonlinear cross-frequency interactions among motor areas. The present study sought to investigate to what extent fMRI- and EEG-based DCM might provide complementary and synergistic insights into neuronal network dynamics. Both modalities share principal similarities in the formulation of the DCM. Thus, we hypothesized that DCM based on induced EEG responses (DCM-IR) and on fMRI would reveal congruent task-dependent network dynamics. Brain electrical (63-channel surface EEG) and Blood Oxygenation Level Dependent (BOLD) signals were recorded in separate sessions from 14 healthy participants performing simple isometric right and left hand grips. DCM-IR and DCM-fMRI were used to estimate coupling parameters modulated by right and left hand grips within a core motor network of six regions comprising bilateral primary motor cortex (M1), ventral premotor cortex (PMv) and supplementary motor area (SMA). We found that DCM-fMRI and DCM-IR similarly revealed significant grip-related increases in facilitatory coupling between SMA and M1 contralateral to the active hand. A grip-dependent interhemispheric reciprocal inhibition between M1 bilaterally was only revealed by DCM-fMRI but not by DCM-IR. Frequency-resolved coupling analysis showed that the information flow from contralateral SMA to M1 was predominantly a linear alpha-to-alpha (9-13Hz) interaction. We also detected some cross-frequency coupling from SMA to contralateral M1, i.e., between lower beta (14-21Hz) at the SMA and higher beta (22-30Hz) at M1 during right hand grip and between alpha (9-13Hz) at SMA and lower beta (14-21Hz) at M1 during left hand grip. In conclusion, the strategy of informing EEG source-space configurations with fMRI-derived coordinates, cross-validating basic connectivity maps and analysing frequency coding allows for deeper insight into the motor network architecture of the human brain. The present results provide evidence for the robustness of non-invasively measured causal information flow from secondary motor areas such as SMA towards M1 and further contribute to the validation of the methodological approach of multimodal DCM to explore human network dynamics.

MEG-based detection and localization of perilesional dysfunction in chronic stroke

Post-stroke impairment is associated not only with structural lesions, but also with dysfunction in surviving perilesional tissue. Previous studies using equivalent current dipole source localization of MEG/EEG signals have demonstrated a preponderance of slow-wave activity localized to perilesional areas. Recent studies have also demonstrated the utility of nonlinear analyses such as multiscale entropy (MSE) for quantifying neuronal dysfunction in a wide range of pathologies. The current study utilized beamformer-based reconstruction of signals in source space to compare spectral and nonlinear measures of electrical activity in perilesional and healthy cortices. Data were collected from chronic stroke patients and healthy controls, both young and elderly. We assessed relative power in the delta (1–4 Hz), theta (4–7 Hz), alpha (8–12 Hz) and beta (15–30 Hz) frequency bands, and also measured the nonlinear complexity of electrical activity using MSE. Perilesional tissue exhibited a general slowing of the power spectrum (increased delta/theta, decreased beta) as well as a reduction in MSE. All measures tested were similarly sensitive to changes in the posterior perilesional regions, but anterior perilesional dysfunction was detected better by MSE and beta power. The findings also suggest that MSE is specifically sensitive to electrophysiological dysfunction in perilesional tissue, while spectral measures were additionally affected by an increase in rolandic beta power with advanced age. Furthermore, perilesional electrophysiological abnormalities in the left hemisphere were correlated with the degree of language task-induced activation in the right hemisphere. Finally, we demonstrate that single subject spectral and nonlinear analyses can identify dysfunctional perilesional regions within individual patients that may be ideal targets for interventions with noninvasive brain stimulation.

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We assessed the spontaneous MEG activity of perilesional tissue in stroke.

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We observed perilesional spectral slowing and reduced signal complexity.

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We demonstrate a method to identify dysfunctional tissue within a single subject.

Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives

Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems extract specific features of brain activity and translate them into control signals that drive an output. Recently, a category of BCIs that are built on the rhythmic activity recorded over the sensorimotor cortex, i.e., the sensorimotor rhythm (SMR), has attracted considerable attention among the BCIs that use noninvasive neural recordings, e.g., electroencephalography (EEG), and have demonstrated the capability of multidimensional prosthesis control. This paper reviews the current state and future perspectives of SMR-based BCI and its clinical applications, in particular focusing on the EEG SMR. The characteristic features of SMR from the human brain are described and their underlying neural sources are discussed. The functional components of SMR-based BCI, together with its current clinical applications, are reviewed. Finally, limitations of SMR-BCIs and future outlooks are also discussed.

Localization of electrophysiological responses to semantic and syntactic anomalies in language comprehension with MEG

Syntactically and semantically anomalous words encountered during sentence comprehension are known to elicit dissociable electrophysiological responses, which are thought to reflect distinct aspects of language processing. However, the sources of these responses have not been well characterized. We used beamforming analysis of magnetoencephalography (MEG) data to map generators of electrophysiological responses to linguistic anomalies. Anomalous words occurred in the context of a sentence acceptability judgement task conducted in both visual and auditory modalities. Time-frequency analysis revealed that both kinds of violations elicited event-related synchronization (ERS) in the delta-theta frequency range (1-5 Hz), and desynchronization (ERD) in the alpha-beta range (8-30 Hz). In addition, these responses were differentially modulated by violation type and presentation modality. 1-5 Hz responses were consistently localized within medial prefrontal cortex and did not vary significantly across violation types, but were stronger for visual presentation. In contrast, 8-30 Hz ERD occurred in different regions for different violation types. For semantic violations the distribution was predominantly in the bilateral occipital cortex and left temporal and inferior frontal regions, and these effects did not differ for visual and auditory presentation. In contrast, syntactic responses were strongly affected by presentation modality. Under visual presentation, syntactic violations elicited bilateral 8-30 Hz ERD extending into dorsal parietal and frontal regions, whereas effects were much weaker and mostly statistically insignificant in the auditory modality. These results suggest that delta-theta ERS reflects generalized increases in working memory demands related to linguistic anomaly detection, while alpha-beta ERD reflects specific activation of cortical regions involved in distinct aspects of linguistic processing, such as semantic vs. phonological short-term memory. Beamforming analysis of time-domain average signals (ERFs) revealed an N400m effect for semantic anomalies in both modalities, localized to left superior temporal and posterior frontal regions, and a later P600-like effect for syntactic anomalies in both modalities, widespread over bilateral frontal, posterior temporal, and parietal regions. These results indicate that time-domain averaged responses and induced oscillatory responses have distinct properties, including localization and modality dependence, and likely reflect dissociable and complementary aspects of neural activity related to language comprehension and additional task-related processes.

The application of electro- and magneto-encephalography in tinnitus research - methods and interpretations

In recent years, there has been a significant increase in the use of electroencephalography (EEG) and magnetoencephalography (MEG) to investigate changes in oscillatory brain activity associated with tinnitus with many conflicting results. Current view of the underlying mechanism of tinnitus is that it results from changes in brain activity in various structures of the brain as a consequence of sensory deprivation. This in turn gives rise to increased spontaneous activity and/or synchrony in the auditory centers but also involves modulation from non-auditory processes from structures of the limbic and paralimbic system. Some of the neural changes associated with tinnitus may be assessed non-invasively in human beings with MEG and EEG (M/EEG) in ways, which are superior to animal studies and other non-invasive imaging techniques. However, both MEG and EEG have their limitations and research results can be misinterpreted without appropriate consideration of these limitations. In this article, I intend to provide a brief review of these techniques, describe what the recorded signals reflect in terms of the underlying neural activity, and their strengths and limitations. I also discuss some pertinent methodological issues involved in tinnitus-related studies and conclude with suggestions to minimize possible discrepancies between results. The overall message is that while MEG and EEG are extremely useful techniques, the interpretation of results from tinnitus studies requires much caution given the individual variability in oscillatory activity and the limits of these techniques.

Recursive approach of EEG-segment-based principal component analysis substantially reduces cryogenic pump artifacts in simultaneous EEG-fMRI data

Electroencephalography (EEG) data simultaneously acquired with functional magnetic resonance imaging (fMRI) data are preprocessed to remove gradient artifacts (GAs) and ballistocardiographic artifacts (BCAs). Nonetheless, these data, especially in the gamma frequency range, can be contaminated by residual artifacts produced by mechanical vibrations in the MRI system, in particular the cryogenic pump that compresses and transports the helium that chills the magnet (the helium-pump). However, few options are available for the removal of helium-pump artifacts. In this study, we propose a recursive approach of EEG-segment-based principal component analysis (rsPCA) that enables the removal of these helium-pump artifacts. Using the rsPCA method, feature vectors representing helium-pump artifacts were successfully extracted as eigenvectors, and the reconstructed signals of the feature vectors were subsequently removed. A test using simultaneous EEG-fMRI data acquired from left-hand (LH) and right-hand (RH) clenching tasks performed by volunteers found that the proposed rsPCA method substantially reduced helium-pump artifacts in the EEG data and significantly enhanced task-related gamma band activity levels (p=0.0038 and 0.0363 for LH and RH tasks, respectively) in EEG data that have had GAs and BCAs removed. The spatial patterns of the fMRI data were estimated using a hemodynamic response function (HRF) modeled from the estimated gamma band activity in a general linear model (GLM) framework. Active voxel clusters were identified in the post-/pre-central gyri of motor area, only from the rsPCA method (uncorrected p<0.001 for both LH/RH tasks). In addition, the superior temporal pole areas were consistently observed (uncorrected p<0.001 for the LH task and uncorrected p<0.05 for the RH task) in the spatial patterns of the HRF model for gamma band activity when the task paradigm and movement were also included in the GLM.

Oscillatory responses to semantic and syntactic violations

EEG studies employing time-frequency analysis have revealed changes in theta and alpha power in a variety of language and memory tasks. Semantic and syntactic violations embedded in sentences evoke well-known ERPs, but little is known about the oscillatory responses to these violations. We investigated oscillatory responses to both kinds of violations, while monolingual and bilingual participants performed an acceptability judgment task. Both violations elicited power decreases (event-related desynchronization, ERD) in the 8-30 Hz frequency range, but with different scalp topographies. In addition, semantic anomalies elicited power increases (event-related synchronization, ERS) in the 1-5 Hz frequency band. The 1-5 Hz ERS was strongly phase-locked to stimulus onset and highly correlated with time domain averages, whereas the 8-30 Hz ERD response varied independently of these. In addition, the results showed that language expertise modulated 8-30 Hz ERD for syntactic violations as a function of the executive demands of the task. When the executive function demands were increased using a grammaticality judgment task, bilinguals but not monolinguals demonstrated reduced 8-30 Hz ERD for syntactic violations. These findings suggest a putative role of the 8-30 Hz ERD response as a marker of linguistic processing that likely represents a separate neural process from those underlying ERPs.

Spatiotemporal dissociation of brain activity underlying subjective awareness, objective performance and confidence

Despite intense recent research, the neural correlates of conscious visual perception remain elusive. The most established paradigm for studying brain mechanisms underlying conscious perception is to keep the physical sensory inputs constant and identify brain activities that correlate with the changing content of conscious awareness. However, such a contrast based on conscious content alone would not only reveal brain activities directly contributing to conscious perception, but also include brain activities that precede or follow it. To address this issue, we devised a paradigm whereby we collected, trial-by-trial, measures of objective performance, subjective awareness, and the confidence level of subjective awareness. Using magnetoencephalography recordings in healthy human volunteers, we dissociated brain activities underlying these different cognitive phenomena. Our results provide strong evidence that widely distributed slow cortical potentials (SCPs) correlate with subjective awareness, even after the effects of objective performance and confidence were both removed. The SCP correlate of conscious perception manifests strongly in its waveform, phase, and power. In contrast, objective performance and confidence were both contributed by relatively transient brain activity. These results shed new light on the brain mechanisms of conscious, unconscious, and metacognitive processing.

Broadband cortical desynchronization underlies the human psychedelic state

Psychedelic drugs produce profound changes in consciousness, but the underlying neurobiological mechanisms for this remain unclear. Spontaneous and induced oscillatory activity was recorded in healthy human participants with magnetoencephalography after intravenous infusion of psilocybin--prodrug of the nonselective serotonin 2A receptor agonist and classic psychedelic psilocin. Psilocybin reduced spontaneous cortical oscillatory power from 1 to 50 Hz in posterior association cortices, and from 8 to 100 Hz in frontal association cortices. Large decreases in oscillatory power were seen in areas of the default-mode network. Independent component analysis was used to identify a number of resting-state networks, and activity in these was similarly decreased after psilocybin. Psilocybin had no effect on low-level visually induced and motor-induced gamma-band oscillations, suggesting that some basic elements of oscillatory brain activity are relatively preserved during the psychedelic experience. Dynamic causal modeling revealed that posterior cingulate cortex desynchronization can be explained by increased excitability of deep-layer pyramidal neurons, which are known to be rich in 5-HT2A receptors. These findings suggest that the subjective effects of psychedelics result from a desynchronization of ongoing oscillatory rhythms in the cortex, likely triggered by 5-HT2A receptor-mediated excitation of deep pyramidal cells.

Non-Gaussian probabilistic MEG source localisation based on kernel density estimation

There is strong evidence to suggest that data recorded from magnetoencephalography (MEG) follows a non-Gaussian distribution. However, existing standard methods for source localisation model the data using only second order statistics, and therefore use the inherent assumption of a Gaussian distribution. In this paper, we present a new general method for non-Gaussian source estimation of stationary signals for localising brain activity from MEG data. By providing a Bayesian formulation for MEG source localisation, we show that the source probability density function (pdf), which is not necessarily Gaussian, can be estimated using multivariate kernel density estimators. In the case of Gaussian data, the solution of the method is equivalent to that of widely used linearly constrained minimum variance (LCMV) beamformer. The method is also extended to handle data with highly correlated sources using the marginal distribution of the estimated joint distribution, which, in the case of Gaussian measurements, corresponds to the null-beamformer. The proposed non-Gaussian source localisation approach is shown to give better spatial estimates than the LCMV beamformer, both in simulations incorporating non-Gaussian signals, and in real MEG measurements of auditory and visual evoked responses, where the highly correlated sources are known to be difficult to estimate.

Adaptive significance of right hemisphere activation in aphasic language comprehension

Aphasic patients often exhibit increased right hemisphere activity during language tasks. This may represent takeover of function by regions homologous to the left-hemisphere language networks, maladaptive interference, or adaptation of alternate compensatory strategies. To distinguish between these accounts, we tested language comprehension in 25 aphasic patients using an online sentence-picture matching paradigm while measuring brain activation with MEG. Linguistic conditions included semantically irreversible ("The boy is eating the apple") and reversible ("The boy is pushing the girl") sentences at three levels of syntactic complexity. As expected, patients performed well above chance on irreversible sentences, and at chance on reversible sentences of high complexity. Comprehension of reversible non-complex sentences ranged from nearly perfect to chance, and was highly correlated with offline measures of language comprehension. Lesion analysis revealed that comprehension deficits for reversible sentences were predicted by damage to the left temporal lobe. Although aphasic patients activated homologous areas in the right temporal lobe, such activation was not correlated with comprehension performance. Rather, patients with better comprehension exhibited increased activity in dorsal fronto-parietal regions. Correlations between performance and dorsal network activity occurred bilaterally during perception of sentences, and in the right hemisphere during a post-sentence memory delay. These results suggest that effortful reprocessing of perceived sentences in short-term memory can support improved comprehension in aphasia, and that strategic recruitment of alternative networks, rather than homologous takeover, may account for some findings of right hemisphere language activation in aphasia.

Functional features of nociceptive-induced suppression of alpha band electroencephalographic oscillations

Nociceptive stimuli can induce a transient suppression of electroencephalographic oscillations in the alpha frequency band (ie, alpha event-related desynchronization, α-ERD). Here we investigated whether α-ERD could be functionally distinguished in 2 temporally and spatially segregated subcomponents as suggested by previous studies. In addition, we tested whether the degree of dependence of nociceptive-induced α-ERD magnitude on the prestimulus α-power would have been larger than the degree of dependence on the poststimulus α-power. Our findings confirmed the dissociation between a sensory-related α-ERD maximally distributed over contralateral central electrodes, and a task-related α-ERD (possibly affected by motor-related activity), maximally distributed at posterior parietal and occipital electrodes. The cortical sources of these activities were estimated to be located at the level of sensorimotor and bilateral occipital cortices, respectively. Importantly, the time course of the α-ERD revealed that functional segregation emerged only at late latencies (400 to 750 ms) whereas topographic similarity was observed at earlier latencies (250 to 350 ms). Furthermore, the nociceptive-induced α-ERD magnitude was significantly more dependent on prestimulus than poststimulus α-power. Altogether these findings provide direct evidence that the nociceptive-induced α-ERD reflects the summation of sensory-related and task-related cortical processes, and that prestimulus fluctuations can remarkably influence the non-phase-locked nociceptive α-ERD.

PERSPECTIVE

Present results extend the functional understanding of α-oscillation suppression during pain perception and demonstrate the influence of prestimulus variability on this cortical phenomenon. This work has the potential to guide pain clinicians in a more accurate interpretation on physiological and psychological modulations of α-oscillations.

Elevating endogenous GABA levels with GAT-1 blockade modulates evoked but not induced responses in human visual cortex

The electroencephalographic/magnetoencephalographic (EEG/MEG) signal is generated primarily by the summation of the postsynaptic currents of cortical principal cells. At a microcircuit level, these glutamatergic principal cells are reciprocally connected to GABAergic interneurons. Here we investigated the relative sensitivity of visual evoked and induced responses to altered levels of endogenous GABAergic inhibition. To do this, we pharmacologically manipulated the GABA system using tiagabine, which blocks the synaptic GABA transporter 1, and so increases endogenous GABA levels. In a single-blinded and placebo-controlled crossover study of 15 healthy participants, we administered either 15 mg of tiagabine or a placebo. We recorded whole-head MEG, while participants viewed a visual grating stimulus, before, 1, 3 and 5 h post tiagabine ingestion. Using beamformer source localization, we reconstructed responses from early visual cortices. Our results showed no change in either stimulus-induced gamma-band amplitude increases or stimulus-induced alpha amplitude decreases. However, the same data showed a 45% reduction in the evoked response component at ∼80 ms. These data demonstrate that, in early visual cortex the evoked response shows a greater sensitivity compared with induced oscillations to pharmacologically increased endogenous GABA levels. We suggest that previous studies correlating GABA concentrations as measured by magnetic resonance spectroscopy to gamma oscillation frequency may reflect underlying variations such as interneuron/inhibitory synapse density rather than functional synaptic GABA concentrations.

High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations

In recent years high-frequency brain activity in the gamma-frequency band (30-80 Hz) and above has become the focus of a growing body of work in MEG/EEG research. Unfortunately, high-frequency neural activity overlaps entirely with the spectral bandwidth of muscle activity (~20-300 Hz). It is becoming appreciated that artifacts of muscle activity may contaminate a number of non-invasive reports of high-frequency activity. In this review, the spectral, spatial, and temporal characteristics of muscle artifacts are compared with those described (so far) for high-frequency neural activity. In addition, several of the techniques that are being developed to help suppress muscle artifacts in MEG/EEG are reviewed. Suggestions are made for the collection, analysis, and presentation of experimental data with the aim of reducing the number of publications in the future that may contain muscle artifacts.

fMRI hemodynamics accurately reflects neuronal timing in the human brain measured by MEG

Neuronal activation sequence information is essential for understanding brain functions. Extracting such timing information from blood oxygenation level dependent (BOLD) fMRI is confounded by interregional neurovascular differences and poorly understood relations between BOLD and electrophysiological response delays. Here, we recorded whole-head BOLD fMRI at 100 ms resolution and magnetoencephalography (MEG) during a visuomotor reaction-time task. Both methods detected the same activation sequence across five regions, from visual towards motor cortices, with linearly correlated interregional BOLD and MEG response delays. The smallest significant interregional BOLD delay was 100 ms; all delays ≥400 ms were significant. Switching the order of external events reversed the sequence of BOLD activations, indicating that interregional neurovascular differences did not confound the results. This may open new avenues for using fMRI to follow rapid activation sequences in the brain.