Interactions between posterior gamma and frontal alpha/beta oscillations during imagined actions

Cross-frequency coupling in real and virtual brain networks

Information processing in the brain is thought to rely on the convergence and divergence of oscillatory behaviors of widely distributed brain areas. This information flow is captured in its simplest form via the concepts of synchronization and desynchronization and related metrics. More complex forms of information flow are transient synchronizations and multi-frequency behaviors with metrics related to cross-frequency coupling (CFC). It is supposed that CFC plays a crucial role in the organization of large-scale networks and functional integration across large distances. In this study, we describe different CFC measures and test their applicability in simulated and real electroencephalographic (EEG) data obtained during resting state. For these purposes, we derive generic oscillator equations from full brain network models. We systematically model and simulate the various scenarios of CFC under the influence of noise to obtain biologically realistic oscillator dynamics. We find that (i) specific CFC-measures detect correctly in most cases the nature of CFC under noise conditions, (ii) bispectrum (BIS) and bicoherence (BIC) correctly detect the CFCs in simulated data, (iii) empirical resting state EEG show a prominent delta-alpha CFC as identified by specific CFC measures and the more classic BIS and BIC. This coupling was mostly asymmetric (directed) and generally higher in the eyes closed (EC) than in the eyes open (EO) condition. In conjunction, these two sets of measures provide a powerful toolbox to reveal the nature of couplings from experimental data and as such allow inference on the brain state dependent information processing. Methodological advantages of using CFC measures and theoretical significance of delta and alpha interactions during resting and other brain states are discussed.

Adaptive filtering methods for identifying cross-frequency couplings in human EEG

Oscillations have been increasingly recognized as a core property of neural responses that contribute to spontaneous, induced, and evoked activities within and between individual neurons and neural ensembles. They are considered as a prominent mechanism for information processing within and communication between brain areas. More recently, it has been proposed that interactions between periodic components at different frequencies, known as cross-frequency couplings, may support the integration of neuronal oscillations at different temporal and spatial scales. The present study details methods based on an adaptive frequency tracking approach that improve the quantification and statistical analysis of oscillatory components and cross-frequency couplings. This approach allows for time-varying instantaneous frequency, which is particularly important when measuring phase interactions between components. We compared this adaptive approach to traditional band-pass filters in their measurement of phase-amplitude and phase-phase cross-frequency couplings. Evaluations were performed with synthetic signals and EEG data recorded from healthy humans performing an illusory contour discrimination task. First, the synthetic signals in conjunction with Monte Carlo simulations highlighted two desirable features of the proposed algorithm vs. classical filter-bank approaches: resilience to broad-band noise and oscillatory interference. Second, the analyses with real EEG signals revealed statistically more robust effects (i.e. improved sensitivity) when using an adaptive frequency tracking framework, particularly when identifying phase-amplitude couplings. This was further confirmed after generating surrogate signals from the real EEG data. Adaptive frequency tracking appears to improve the measurements of cross-frequency couplings through precise extraction of neuronal oscillations.

Cross-frequency coupling within and between the human thalamus and neocortex

There is currently growing interest in, and increasing evidence for, cross-frequency interactions between electrical field oscillations in the brains of various organisms. A number of theories have linked such interactions to crucial features of neuronal function and cognition. In mammals, these interactions have mostly been reported in the neocortex and hippocampus, and it remains unexplored whether similar patterns of activity occur in the thalamus, and between the thalamus and neocortex. Here we use data recorded from patients undergoing thalamic deep-brain stimulation for epilepsy to demonstrate the existence and prevalence, across a range of frequencies, of both phase–amplitude (PAC) and amplitude–amplitude coupling (AAC) both within the thalamus and prefrontal cortex (PFC), and between them. These cross-frequency interactions may play an important role in local processing within the thalamus and neocortex, as well as information transfer between them.

Evidence for a motor gamma-band network governing response interference

The gamma-band response is thought to represent a key neural signature of information processing in the human brain. These brain signals have been associated with a variety of sensory modalities (vision, sensation, and audition) and also following basic motor responses, yet the functional significance of the motor gamma-band response remains unclear. We used the Multi-Source Interference Task (MSIT) to assess the sensitivity of these cortical motor gamma-band rhythms to stimuli producing response interference. We recorded MEG from adult participants (N=24) during MSIT task performance and compared motor gamma-band activity on Control and Interference trials. Reaction time on MSIT Interference trials was significantly longer (~0.2 s) for all subjects. Response interference produced a significant increase in motor gamma-band activity including ~0.5 s sustained increase in gamma-band activity from contralateral primary motor area directly preceding the response. In addition, activation of increased right Inferior Frontal Gyrus (R-IFG) was observed at gamma-band frequencies ~0.2 s prior to the button press response. Post-hoc analysis of R-IFG gamma-band activity was observed to correlate with reaction time increases to response interference. Our study is the first to record MEG during MSIT task performance. We observed novel activity of the motor gamma-band on interference trials which was sustained prior to the response and in novel locations including contralateral (BA6), and R-IFG. Our results support the idea that R-IFG is specialized structure for response control that also functions at gamma-band frequencies. Together, these data provide evidence for a motor gamma-band network for response selection and maintenance of planned behavior.

Local and long-range functional connectivity is reduced in concert in autism spectrum disorders

Long-range cortical functional connectivity is often reduced in autism spectrum disorders (ASD), but the nature of local cortical functional connectivity in ASD has remained elusive. We used magnetoencephalography to measure task-related local functional connectivity, as manifested by coupling between the phase of alpha oscillations and the amplitude of gamma oscillations, in the fusiform face area (FFA) of individuals diagnosed with ASD and typically developing individuals while they viewed neutral faces, emotional faces, and houses. We also measured task-related long-range functional connectivity between the FFA and the rest of the cortex during the same paradigm. In agreement with earlier studies, long-range functional connectivity between the FFA and three distant cortical regions was reduced in the ASD group. However, contrary to the prevailing hypothesis in the field, we found that local functional connectivity within the FFA was also reduced in individuals with ASD when viewing faces. Furthermore, the strength of long-range functional connectivity was directly correlated to the strength of local functional connectivity in both groups; thus, long-range and local connectivity were reduced proportionally in the ASD group. Finally, the magnitude of local functional connectivity correlated with ASD severity, and statistical classification using local and long-range functional connectivity data identified ASD diagnosis with 90% accuracy. These results suggest that failure to entrain neuronal assemblies fully both within and across cortical regions may be characteristic of ASD.

Interactions between visual and motor areas during the recognition of plausible actions as revealed by magnetoencephalography

Several studies have shown activation of the mirror neuron system (MNS), comprising the temporal, posterior parietal, and sensorimotor areas when observing plausible actions, but far less is known on how these cortical areas interact during the recognition of a plausible action. Here, we recorded neural activity with magnetoencephalography while subjects viewed point-light displays of biologically plausible and scrambled versions of actions. We were interested in modulations of oscillatory activity and, specifically, in coupling of oscillatory activity between visual and motor areas. Both plausible and scrambled actions elicited modulations of θ (5-7 Hz), α (7-13 Hz), β (13-35 Hz), and γ (55-100 Hz) power within visual and motor areas. When comparing between the two actions, we observed sequential and spatially distinct increases of γ (∼65 Hz), β (∼25 Hz), and α (∼11 Hz) power between 0.5 and 1.3 s in parieto-occipital, sensorimotor, and left temporal areas. In addition, significant clusters of γ (∼65 Hz) and α/β (∼15 Hz) power decrease were observed in right temporal and parieto-occipital areas between 1.3 and 2.0 s. We found β-power in sensorimotor areas to be positively correlated on a trial-by-trial basis with parieto-occipital γ and left temporal α-power for the plausible but not for the scrambled condition. These results provide new insights in the neuronal oscillatory activity of the areas involved in the recognition of plausible action movements and their interaction. The power correlations between specific areas underscore the importance of interactions between visual and motor areas of the MNS during the recognition of a plausible action.

Compensatory activity in the extrastriate body area of Parkinson's disease patients

Compensatory mechanisms are a crucial component of the cerebral changes triggered by neurodegenerative disorders. Identifying such compensatory mechanisms requires at least two complementary approaches: localizing candidate areas using functional imaging, and showing that interference with these areas has behavioral consequences. Building on recent imaging evidence, we use this approach to test whether a visual region in the human occipito-temporal cortex-the extrastriate body area-compensates for altered dorsal premotor activity in Parkinson's disease (PD) during motor-related processes. We separately inhibited the extrastriate body area and dorsal premotor cortex in 11 PD patients and 12 healthy subjects, using continuous theta burst stimulation. Our goal was to test whether these areas are involved in motor compensatory processes. We used motor imagery to isolate a fundamental element of motor planning, namely subjects' ability to incorporate the current state of their body into a motor plan (mental hand rotation). We quantified this ability through a posture congruency effect (i.e., the improvement in subjects' performance when their current body posture is congruent to the imagined movement). Following inhibition of the right extrastriate body area, the posture congruency effect was lost in PD patients, but not in healthy subjects. In contrast, inhibition of the left dorsal premotor cortex reduced the posture congruency effect in healthy subjects, but not in PD patients. These findings suggest that the right extrastriate body area plays a compensatory role in PD by supporting a function that is no longer performed by the dorsal premotor cortex.

MEG-SIM: a web portal for testing MEG analysis methods using realistic simulated and empirical data

MEG and EEG measure electrophysiological activity in the brain with exquisite temporal resolution. Because of this unique strength relative to noninvasive hemodynamic-based measures (fMRI, PET), the complementary nature of hemodynamic and electrophysiological techniques is becoming more widely recognized (e.g., Human Connectome Project). However, the available analysis methods for solving the inverse problem for MEG and EEG have not been compared and standardized to the extent that they have for fMRI/PET. A number of factors, including the non-uniqueness of the solution to the inverse problem for MEG/EEG, have led to multiple analysis techniques which have not been tested on consistent datasets, making direct comparisons of techniques challenging (or impossible). Since each of the methods is known to have their own set of strengths and weaknesses, it would be beneficial to quantify them. Toward this end, we are announcing the establishment of a website containing an extensive series of realistic simulated data for testing purposes ( http://cobre.mrn.org/megsim/ ). Here, we present: 1) a brief overview of the basic types of inverse procedures; 2) the rationale and description of the testbed created; and 3) cases emphasizing functional connectivity (e.g., oscillatory activity) suitable for a wide assortment of analyses including independent component analysis (ICA), Granger Causality/Directed transfer function, and single-trial analysis.

"Too Many betas do not Spoil the Broth": The Role of Beta Brain Oscillations in Language Processing

Over the past 20 years, brain oscillations have proven to be a gateway to the understanding of cognitive processes. It has been shown that different neurocognitive aspects of language processing are associated with brain oscillations at various frequencies. Frequencies in the beta range (13–30 Hz) turned out to be particularly important with respect to cognitive and linguistic manipulations during language processing. Beta activity has been involved in higher-order linguistic functions such as the discrimination of word categories and the retrieval of action semantics as well as semantic memory, and syntactic binding processes, which support meaning construction during sentence processing. From a neurophysiological point of view, the important role of the beta frequencies for such a complex cognitive task as language processing seems reasonable. Experimental evidence suggests that frequencies in the beta range are ideal for maintaining and preserving the activity of neuronal assemblies over time. In particular, recent computational and experimental evidence suggest that beta frequencies are important for linking past and present input and the detection of novelty of stimuli, which are essential processes for language perception as well as production. In addition, the beta frequency’s role in the formation of cell assemblies underlying short-term memory seems indispensable for language analysis. Probably the most important point is the well-known relation of beta oscillations with motor processes. It can be speculated that beta activities reflect the close relationship between language comprehension and motor functions, which is one of the core claims of current theories on embodied cognition. In this article, the importance of beta oscillations for language processing is reviewed based both on findings in psychophysiological and neurophysiological literature.

Connectivity measures applied to human brain electrophysiological data

Connectivity measures are (typically bivariate) statistical measures that may be used to estimate interactions between brain regions from electrophysiological data. We review both formal and informal descriptions of a range of such measures, suitable for the analysis of human brain electrophysiological data, principally electro- and magnetoencephalography. Methods are described in the space-time, space-frequency, and space-time-frequency domains. Signal processing and information theoretic measures are considered, and linear and nonlinear methods are distinguished. A novel set of cross-time-frequency measures is introduced, including a cross-time-frequency phase synchronization measure.

Spectral fingerprints of large-scale neuronal interactions

Cognition results from interactions among functionally specialized but widely distributed brain regions; however, neuroscience has so far largely focused on characterizing the function of individual brain regions and neurons therein. Here we discuss recent studies that have instead investigated the interactions between brain regions during cognitive processes by assessing correlations between neuronal oscillations in different regions of the primate cerebral cortex. These studies have opened a new window onto the large-scale circuit mechanisms underlying sensorimotor decision-making and top-down attention. We propose that frequency-specific neuronal correlations in large-scale cortical networks may be 'fingerprints' of canonical neuronal computations underlying cognitive processes.

Modulation of gamma and theta spectral amplitude and phase synchronization is associated with the development of visuo-motor learning

The formation of new motor memories, which is fundamental for efficient performance during adaptation to a visuo-motor rotation, occurs when accurate planning is achieved mostly with feedforward mechanisms. The dynamics of brain activity underlying the switch from feedback to feedforward control is still matter of debate. Based on the results of studies in declarative learning, it is likely that phase synchronization of low and high frequencies as well as their temporal modulation in power amplitude underlie the formation of new motor memories during visuo-motor adaptation. High-density EEG (256 electrodes) was recorded in 17 normal human subjects during adaptation to a visuo-motor rotation of 60° in four incremental steps of 15°. We found that initial learning is associated with enhancement of gamma power in a right parietal region during movement execution as well as gamma/theta phase coherence during movement planning. Late stages of learning are instead accompanied by an increase of theta power over that same right parietal region during movement planning, which is correlated with the degree of learning and retention. Altogether, these results suggest that the formation of new motor memories and, thus, the switch from feedback to feedforward control is associated with the modulation of gamma and theta spectral activities, with respect to their amplitude and phase, during movement planning and execution. Specifically, we propose that gamma/theta phase coupling plays a pivotal role in the integration of a new representation into motor memories.

The neocortical network representing associative memory reorganizes with time in a process engaging the anterior temporal lobe

During encoding, the distributed neocortical representations of memory components are presumed to be associatively linked by the hippocampus. With time, a reorganization of brain areas supporting memory takes place, which can ultimately result in memories becoming independent of the hippocampus. While it is theorized that with time, the neocortical representations become linked by higher order neocortical association areas, this remains to be experimentally supported. In this study, 24 human participants encoded sets of face-location associations, which they retrieved 1 or 25 h later ("recent" and "remote" conditions, respectively), while their brain activity was recorded using whole-head magnetoencephalography. We investigated changes in the functional interactions between the neocortical representational areas emerging over time. To assess functional interactions, trial-by-trial high gamma (60-140 Hz) power correlations were calculated between the neocortical representational areas relevant to the encoded information, namely the fusiform face area (FFA) and posterior parietal cortex (PPC). With time, both the FFA and the PPC increased their functional interactions with the anterior temporal lobe (ATL). Given that the ATL is involved in semantic representation of paired associates, our results suggest that, already within 25 h after acquiring new memory associations, neocortical functional links are established via higher order semantic association areas.

TRENTOOL: a Matlab open source toolbox to analyse information flow in time series data with transfer entropy

Background

Transfer entropy (TE) is a measure for the detection of directed interactions. Transfer entropy is an information theoretic implementation of Wiener's principle of observational causality. It offers an approach to the detection of neuronal interactions that is free of an explicit model of the interactions. Hence, it offers the power to analyze linear and nonlinear interactions alike. This allows for example the comprehensive analysis of directed interactions in neural networks at various levels of description. Here we present the open-source MATLAB toolbox TRENTOOL that allows the user to handle the considerable complexity of this measure and to validate the obtained results using non-parametrical statistical testing. We demonstrate the use of the toolbox and the performance of the algorithm on simulated data with nonlinear (quadratic) coupling and on local field potentials (LFP) recorded from the retina and the optic tectum of the turtle (Pseudemys scripta elegans) where a neuronal one-way connection is likely present.

Results

In simulated data TE detected information flow in the simulated direction reliably with false positives not exceeding the rates expected under the null hypothesis. In the LFP data we found directed interactions from the retina to the tectum, despite the complicated signal transformations between these stages. No false positive interactions in the reverse directions were detected.

Conclusions

TRENTOOL is an implementation of transfer entropy and mutual information analysis that aims to support the user in the application of this information theoretic measure. TRENTOOL is implemented as a MATLAB toolbox and available under an open source license (GPL v3). For the use with neural data TRENTOOL seamlessly integrates with the popular FieldTrip toolbox.

Components of cross-frequency modulation in health and disease

The cognitive deficits associated with schizophrenia are commonly believed to arise from the abnormal temporal integration of information, however a quantitative approach to assess network coordination is lacking. Here, we propose to use cross-frequency modulation (cfM), the dependence of local high-frequency activity on the phase of widespread low-frequency oscillations, as an indicator of network coordination and functional integration. In an exploratory analysis based on pre-existing data, we measured cfM from multi-channel EEG recordings acquired while schizophrenia patients (n = 47) and healthy controls (n = 130) performed an auditory oddball task. Novel application of independent component analysis (ICA) to modulation data delineated components with specific spatial and spectral profiles, the weights of which showed covariation with diagnosis. Global cfM was significantly greater in healthy controls (F_1,175 = 9.25, P < 0.005), while modulation at fronto-temporal electrodes was greater in patients (F_1,175 = 17.5, P < 0.0001). We further found that the weights of schizophrenia-relevant components were associated with genetic polymorphisms at previously identified risk loci. Global cfM decreased with copies of 957C allele in the gene for the dopamine D2 receptor (r = −0.20, P < 0.01) across all subjects. Additionally, greater “aberrant” fronto-temporal modulation in schizophrenia patients was correlated with several polymorphisms in the gene for the α2-subunit of the GABA_A receptor (GABRA2) as well as the total number of risk alleles in GABRA2 (r = 0.45, P < 0.01). Overall, our results indicate great promise for this approach in establishing patterns of cfM in health and disease and elucidating the roles of oscillatory interactions in functional connectivity.

Spatial dependency of action simulation

In this study, we investigated the spatial dependency of action simulation. From previous research in the field of single-cell recordings, grasping studies and from crossmodal extinction tasks, it is known that our surrounding space can be divided into a peripersonal space and extrapersonal space. These two spaces are functionally different at both the behavioral and neuronal level. The peripersonal space can be seen as an action space which is limited to the area in which we can grasp objects without moving the object or ourselves. The extrapersonal space is the space beyond the peripersonal space. Objects situated within peripersonal space are mapped onto an egocentric reference frame. This mapping is thought to be accomplished by action simulation. To provide direct evidence of the embodied nature of this simulated motor act, we performed two experiments, in which we used two mental rotation tasks, one with stimuli of hands and one with stimuli of graspable objects. Stimuli were presented in both peri- and extrapersonal space. The results showed increased reaction times for biomechanically difficult to adopt postures compared to more easy to adopt postures for both hand and graspable object stimuli. Importantly, this difference was only present for stimuli presented in peripersonal space but not for the stimuli presented in extrapersonal space. These results extend previous behavioral findings on the functional distinction between peripersonal- and extrapersonal space by providing direct evidence for the spatial dependency of the use of action simulation. Furthermore, these results strengthen the hypothesis that objects situated within the peripersonal space are mapped onto an egocentric reference frame by action simulation.

Neural evidence for compromised motor imagery in right hemiparetic cerebral palsy

In the present event-related potential (ERP) study we investigated the neural and temporal dynamics of motor imagery in participants with right-sided hemiparetic cerebral palsy (HCP; n = 10) and in left-handed control participants (n = 10). A mental rotation task was used in which participants were required to judge the laterality of hand pictures. At a behavioral level participants with HCP were slower in making hand laterality judgments compared to control subjects, especially when presented with pictures representing the affected hand. At a neural level, individuals with HCP were characterized by a reduced rotation-related negativity (RRN) over parietal areas, that was delayed in onset with respect to control participants. Interestingly, participants that were relatively mildly impaired showed a stronger RRN for the rotation of right-hand stimuli than participants that were more strongly impaired in their motor function, suggesting a direct relation between the motor imagery process and the biomechanical constraints of the participant. Together, the results provide new insights in the relation between motor imagery and motor capabilities and indicate that participants with HCP may be characterized by a compromised ability to use motor imagery.

Hemicraniectomy: a new model for human electrophysiology with high spatio-temporal resolution

Human electrophysiological research is generally restricted to scalp EEG, magneto-encephalography, and intracranial electrophysiology. Here we examine a unique patient cohort that has undergone decompressive hemicraniectomy, a surgical procedure wherein a portion of the calvaria is removed for several months during which time the scalp overlies the brain without intervening bone. We quantify the differences in signals between electrodes over areas with no underlying skull and scalp EEG electrodes over the intact skull in the same subjects. Signals over the hemicraniectomy have enhanced amplitude and greater task-related power at higher frequencies (60-115 Hz) compared with signals over skull. We also provide evidence of a metric for trial-by-trial EMG/EEG coupling that is effective over the hemicraniectomy but not intact skull at frequencies >60 Hz. Taken together, these results provide evidence that the hemicraniectomy model provides a means for studying neural dynamics in humans with enhanced spatial and temporal resolution.

Shaping functional architecture by oscillatory alpha activity: gating by inhibition

In order to understand the working brain as a network, it is essential to identify the mechanisms by which information is gated between regions. We here propose that information is gated by inhibiting task-irrelevant regions, thus routing information to task-relevant regions. The functional inhibition is reflected in oscillatory activity in the alpha band (8-13 Hz). From a physiological perspective the alpha activity provides pulsed inhibition reducing the processing capabilities of a given area. Active processing in the engaged areas is reflected by neuronal synchronization in the gamma band (30-100 Hz) accompanied by an alpha band decrease. According to this framework the brain could be studied as a network by investigating cross-frequency interactions between gamma and alpha activity. Specifically the framework predicts that optimal task performance will correlate with alpha activity in task-irrelevant areas. In this review we will discuss the empirical support for this framework. Given that alpha activity is by far the strongest signal recorded by EEG and MEG, we propose that a major part of the electrophysiological activity detected from the working brain reflects gating by inhibition.

Rhythmic pulsing: linking ongoing brain activity with evoked responses

The conventional assumption in human cognitive electrophysiology using EEG and MEG is that the presentation of a particular event such as visual or auditory stimuli evokes a “turning on” of additional brain activity that adds to the ongoing background activity. Averaging multiple event-locked trials is thought to result in the cancellation of the seemingly random phased ongoing activity while leaving the evoked response. However, recent work strongly challenges this conventional view and demonstrates that the ongoing activity is not averaged out due to specific non-sinusoidal properties. As a consquence, systematic modulations in ongoing activity can produce slow cortical evoked responses reflecting cognitive processing. In this review we introduce the concept of “rhythmic pulsing” to account for this specific non-sinusoidal property. We will explain how rhythmic pulsing can create slow evoked responses from a physiological perspective. We will also discuss how the notion of rhythmic pulsing provides a unifying framework linking ongoing oscillations, evoked responses and the brain's capacity to process incoming information.

An extended motor network generates beta and gamma oscillatory perturbations during development

This study examines the time course and neural generators of oscillatory beta and gamma motor responses in typically-developing children. Participants completed a unilateral flexion-extension task using each index finger as whole-head magnetoencephalography (MEG) data were acquired. These MEG data were imaged in the frequency-domain using spatial filtering and the resulting event-related synchronizations and desynchronizations (ERS/ERD) were subjected to voxel-wise statistical analyses to illuminate time-frequency specific activation patterns. Consistent with adult data, these children exhibited a pre-movement ERD that was strongest over the contralateral post-central gyrus, and a post-movement ERS response with the most prominent peak being in the contralateral precentral gyrus near premotor cortices. We also observed a high-frequency (approximately 80 Hz) ERS response that coincided with movement onset and was centered on the contralateral precentral gyrus, slightly superior and posterior to the beta ERS. In addition to pre- and post-central gyri activations, these children exhibited beta and gamma activity in supplementary motor areas (SMA) before and during movement, and beta activation in cerebellar cortices before and after movement. We believe the gamma synchronization may be an excellent candidate signal of basic cortical motor control, as the spatiotemporal dynamics indicate the primary motor cortex generates this response (and not the beta oscillations) which is closely yoked to the initial muscle activation. Lastly, these data suggest several additional neural regions including the SMA and cerebellum are involved in basic movements during development.

Temporal dynamics of spontaneous MEG activity in brain networks

Functional MRI (fMRI) studies have shown that low-frequency (<0.1 Hz) spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal during restful wakefulness are coherent within distributed large-scale cortical and subcortical networks (resting state networks, RSNs). The neuronal mechanisms underlying RSNs remain poorly understood. Here, we describe magnetoencephalographic correspondents of two well-characterized RSNs: the dorsal attention and the default mode networks. Seed-based correlation mapping was performed using time-dependent MEG power reconstructed at each voxel within the brain. The topography of RSNs computed on the basis of extended (5 min) epochs was similar to that observed with fMRI but confined to the same hemisphere as the seed region. Analyses taking into account the nonstationarity of MEG activity showed transient formation of more complete RSNs, including nodes in the contralateral hemisphere. Spectral analysis indicated that RSNs manifest in MEG as synchronous modulation of band-limited power primarily within the theta, alpha, and beta bands-that is, in frequencies slower than those associated with the local electrophysiological correlates of event-related BOLD responses.

Occipital gamma activation during Vipassana meditation

Long-term Vipassana meditators sat in meditation vs. a control rest (mind-wandering) state for 21 min in a counterbalanced design with spontaneous EEG recorded. Meditation state dynamics were measured with spectral decomposition of the last 6 min of the eyes-closed silent meditation compared to control state. Meditation was associated with a decrease in frontal delta (1-4 Hz) power, especially pronounced in those participants not reporting drowsiness during meditation. Relative increase in frontal theta (4-8 Hz) power was observed during meditation, as well as significantly increased parieto-occipital gamma (35-45 Hz) power, but no other state effects were found for the theta (4-8 Hz), alpha (8-12 Hz), or beta (12-25 Hz) bands. Alpha power was sensitive to condition order, and more experienced meditators exhibited no tendency toward enhanced alpha during meditation relative to the control task. All participants tended to exhibit decreased alpha in association with reported drowsiness. Cross-experimental session occipital gamma power was the greatest in meditators with a daily practice of 10+ years, and the meditation-related gamma power increase was similarly the strongest in such advanced practitioners. The findings suggest that long-term Vipassana meditation contributes to increased occipital gamma power related to long-term meditational expertise and enhanced sensory awareness.

Accumulation of evidence during sequential decision making: the importance of top-down factors

In the last decade, great progress has been made in characterizing the accumulation of neural information during simple unitary perceptual decisions. However, much less is known about how sequentially presented evidence is integrated over time for successful decision making. The aim of this study was to study the mechanisms of sequential decision making in humans. In a magnetoencephalography (MEG) study, we presented healthy volunteers with sequences of centrally presented arrows. Sequence length varied between one and five arrows, and the accumulated directions of the arrows informed the subject about which hand to use for a button press at the end of the sequence (e.g., LRLRR should result in a right-hand press). Mathematical modeling suggested that nonlinear accumulation was the rational strategy for performing this task in the presence of no or little noise, whereas quasilinear accumulation was optimal in the presence of substantial noise. MEG recordings showed a correlate of evidence integration over parietal and central cortex that was inversely related to the amount of accumulated evidence (i.e., when more evidence was accumulated, neural activity for new stimuli was attenuated). This modulation of activity likely reflects a top-down influence on sensory processing, effectively constraining the influence of sensory information on the decision variable over time. The results indicate that, when making decisions on the basis of sequential information, the human nervous system integrates evidence in a nonlinear manner, using the amount of previously accumulated information to constrain the accumulation of additional evidence.

Task-related gamma-band dynamics from an intracerebral perspective: review and implications for surface EEG and MEG

Although non-invasive techniques provide functional activation maps at ever-growing spatio-temporal precision, invasive recordings offer a unique opportunity for direct investigations of the fine-scale properties of neural mechanisms in focal neuronal populations. In this review we provide an overview of the field of intracranial Electroencephalography (iEEG) and discuss its strengths and limitations and its relationship to non-invasive brain mapping techniques. We discuss the characteristics of invasive data acquired from implanted epilepsy patients using stereotactic-electroencephalography (SEEG) and electrocorticography (ECoG) and the use of spectral analysis to reveal task-related modulations in multiple frequency components. Increasing evidence suggests that gamma-band activity (>40 Hz) might be a particularly efficient index for functional mapping. Moreover, the detection of high gamma activity may play a crucial role in bridging the gap between electrophysiology and functional imaging studies as well as in linking animal and human data. The present review also describes recent advances in real-time invasive detection of oscillatory modulations (including gamma activity) in humans. Furthermore, the implications of intracerebral findings on future non-invasive studies are discussed.

Increased dependence of action selection on recent motor history in Parkinson's disease

It is well known that the basal ganglia are involved in switching between movement sequences. Here we test the hypothesis that this contribution is an instance of a more general role of the basal ganglia in selecting actions that deviate from the context defined by the recent motor history, even when there is no sequential structure to learn or implement. We investigated the effect of striatal dopamine depletion [in Parkinson's disease (PD)] on the ability to switch between independent action plans. PD patients with markedly lateralized signs performed a hand laterality judgment task that involved action selection of their most and least affected hand. Trials where patients selected the same (repeat) or the alternative (switch) hand as in a previous trial were compared, and this was done separately for the most and least affected hand. Behaviorally, PD patients showed switch-costs that were specific to the most affected hand and that increased with disease severity. Functional magnetic resonance imaging (fMRI) showed that this behavioral effect was related to the state of the frontostriatal system: as disease severity increased, contributions of the basal ganglia to the selection process and their effective connectivity with the medial frontal cortex (MFC) decreased, whereas involvement of the MFC increased. We conclude that the basal ganglia are important for rapidly switching toward novel motor plans even when there is no sequential structure to learn or implement. The enhanced MFC activity may result either from reduced focusing abilities of the basal ganglia or from compensatory processes.