High-frequency oscillations in distributed neural networks reveal the dynamics of human decision making

Brain sources' activity in resting state before a visuo-motor task

Objective In modern neuroscience, the underlying mechanisms of the elaboration and reaction to different kinds of stimuli of the brain hemispheres remain still very challenging to understand, together with the possibility to anticipate certain behaviors to improve the performance. Approach The purpose of the present study was to investigate the brain rhythms characteristics of EEG recordings and in particular, their interhemispheric differences in resting state condition before a visuo-motor task in a population of healthy adults. During the task, subjects were asked to react to a sequence of visual cues as quick as possible. The reaction times (RTs) to the task were measured, collected and correlated with the EEG signals recorded in a resting state condition immediately preceding the task. The EEG data were analyzed in the space of cortical sources of EEG rhythms by the computation of the Global Spectra Power Density (GSPD) in the left and in the right hemisphere, and of an index of brain Laterality L. Main results The results showed a negative correlation between the RTs and the GSPD in the central areas in the left and in the right hemisphere in both eyes open and eyes closed conditions. A close to significant and negative correlation was found in the parietal areas. Furthermore, RTs negatively correlated with L in the central areas in eyes closed condition. The results showed a negative correlation between the RTs and the GSPD in the central areas in the left and in the right hemisphere in both eyes open and eyes closed conditions. Significance The correlations between the brain activity before a task and the RTs to the task can represent an interesting tool for exploring the brain state characterization for the upcoming tasks performance.

NUTMEG: Open Source Software for M/EEG Source Reconstruction

Neurodynamic Utility Toolbox for Magnetoencephalo- and Electroencephalography (NUTMEG) is an open-source MATLAB-based toolbox for the analysis and reconstruction of magnetoencephalography/electroencephalography data in source space. NUTMEG includes a variety of options for the user in data import, preprocessing, source reconstruction, and functional connectivity. A group analysis toolbox allows the user to run a variety of inferential statistics on their data in an easy-to-use GUI-driven format. Importantly, NUTMEG features an interactive five-dimensional data visualization platform. A key feature of NUTMEG is the availability of a large menu of interference cancelation and source reconstruction algorithms. Each NUTMEG operation acts as a stand-alone MATLAB function, allowing the package to be easily adaptable and scripted for the more advanced user for interoperability with other software toolboxes. Therefore, NUTMEG enables a wide range of users access to a complete "sensor-to- source-statistics" analysis pipeline.

Can EEG and MEG detect signals from the human cerebellum?

The cerebellum plays a key role in the regulation of motor learning, coordination and timing, and has been implicated in sensory and cognitive processes as well. However, our current knowledge of its electrophysiological mechanisms comes primarily from direct recordings in animals, as investigations into cerebellar function in humans have instead predominantly relied on lesion, haemodynamic and metabolic imaging studies. While the latter provide fundamental insights into the contribution of the cerebellum to various cerebellar-cortical pathways mediating behaviour, they remain limited in terms of temporal and spectral resolution. In principle, this shortcoming could be overcome by monitoring the cerebellum's electrophysiological signals. Non-invasive assessment of cerebellar electrophysiology in humans, however, is hampered by the limited spatial resolution of electroencephalography (EEG) and magnetoencephalography (MEG) in subcortical structures, i.e., deep sources. Furthermore, it has been argued that the anatomical configuration of the cerebellum leads to signal cancellation in MEG and EEG. Yet, claims that MEG and EEG are unable to detect cerebellar activity have been challenged by an increasing number of studies over the last decade. Here we address this controversy and survey reports in which electrophysiological signals were successfully recorded from the human cerebellum. We argue that the detection of cerebellum activity non-invasively with MEG and EEG is indeed possible and can be enhanced with appropriate methods, in particular using connectivity analysis in source space. We provide illustrative examples of cerebellar activity detected with MEG and EEG. Furthermore, we propose practical guidelines to optimize the detection of cerebellar activity with MEG and EEG. Finally, we discuss MEG and EEG signal contamination that may lead to localizing spurious sources in the cerebellum and suggest ways of handling such artefacts. This review is to be read as a perspective review that highlights that it is indeed possible to measure cerebellum with MEG and EEG and encourages MEG and EEG researchers to do so. Its added value beyond highlighting and encouraging is that it offers useful advice for researchers aspiring to investigate the cerebellum with MEG and EEG.

New Horizons in Early Dementia Diagnosis: Can Cerebellar Stimulation Untangle the Knot?

Differentiating Mild Cognitive Impairment (MCI) from dementia and estimating the risk of MCI-to-dementia conversion (MDC) are challenging tasks. Thus, objective tools are mandatory to get early diagnosis and prognosis. About that, there is a growing interest on the role of cerebellum-cerebrum connectivity (CCC). The aim of this study was to differentiate patients with an early diagnosis of dementia and MCI depending on the effects of a transcranial magnetic stimulation protocol (intermittent theta-burst stimulation -iTBS) delivered on the cerebellum able to modify cortico-cortical connectivity. Indeed, the risk of MDC is related to the response to iTBS, being higher in non-responder individuals. All patients with MCI, but eight (labelled as MCI-), showed preserved iTBS aftereffect. Contrariwise, none of the patients with dementia showed iTBS aftereffects. None of the patients showed EEG aftereffects following a sham TBS protocol. Five among the MCI- patients converted to dementia at 6-month follow-up. Our data suggest that cerebellar stimulation by means of iTBS may support the differential diagnosis between MCI and dementia and potentially identify the individuals with MCI who may be at risk of MDC. These findings may help clinicians to adopt a better prevention/follow-up strategy in such patients.

Risk-taking bias in human decision-making is encoded via a right-left brain push-pull system

Biases and fallacies can nudge humans in one direction or another as they make decisions. During gambling, bias is often generated by internal factors, including individual preferences, past experience, or emotions, and can move a person toward or away from risky behavior. The neural mechanisms responsible for generating internal bias are largely unknown, limiting the treatment of patients with neurological diseases that impair decision-making. We applied mathematical modeling techniques and high-resolution intracerebral recordings to uncover how a hidden internal bias builds up from past experiences to influence decisions and where this internal bias is encoded in the brain. Our findings suggest that biology exploits a distributed lateralized push–pull neural system to govern counterintuitive and highly variable decision-making in humans.

A person’s decisions vary even when options stay the same, like when a gambler changes bets despite constant odds of winning. Internal bias (e.g., emotion) contributes to this variability and is shaped by past outcomes, yet its neurobiology during decision-making is not well understood. To map neural circuits encoding bias, we administered a gambling task to 10 participants implanted with intracerebral depth electrodes in cortical and subcortical structures. We predicted the variability in betting behavior within and across patients by individual bias, which is estimated through a dynamical model of choice. Our analysis further revealed that high-frequency activity increased in the right hemisphere when participants were biased toward risky bets, while it increased in the left hemisphere when participants were biased away from risky bets. Our findings provide electrophysiological evidence that risk-taking bias is a lateralized push–pull neural system governing counterintuitive and highly variable decision-making in humans.

Oscillatory Cortical Activity in an Animal Model of Dystonia Caused by Cerebellar Dysfunction

The synchronization of neuronal activity in the sensorimotor cortices is crucial for motor control and learning. This synchrony can be modulated by upstream activity in the cerebello-cortical network. However, many questions remain over the details of how the cerebral cortex and the cerebellum communicate. Therefore, our aim is to study the contribution of the cerebellum to oscillatory brain activity, in particular in the case of dystonia, a severely disabling motor disease associated with altered sensorimotor coupling. We used a kainic-induced dystonia model to evaluate cerebral cortical oscillatory activity and connectivity during dystonic episodes. We performed microinjections of low doses of kainic acid into the cerebellar vermis in mice and examined activities in somatosensory, motor and parietal cortices. We showed that repeated applications of kainic acid into the cerebellar vermis, for five consecutive days, generate reproducible dystonic motor behavior. No epileptiform activity was recorded on electrocorticogram (ECoG) during the dystonic postures or movements. We investigated the ECoG power spectral density and coherence between motor cortex, somatosensory and parietal cortices before and during dystonic attacks. During the baseline condition, we found a phenomenon of permanent adaptation with a change of baseline locomotor activity coupled to an ECoG gamma band increase in all cortices. In addition, after kainate administration, we observed an increase in muscular activity, but less signs of dystonia together with modulations of the ECoG power spectra with an increase in gamma band in motor, parietal and somatosensory cortices. Moreover, we found reduced coherence in all measured frequency bands between the motor cortex and somatosensory or parietal cortices compared to baseline. In conclusion, examination of cortical oscillatory activities in this animal model of chronic dystonia caused by cerebellar dysfunction reveals a disruption in the coordination of neuronal activity across the cortical sensorimotor/parietal network, which may underlie motor skill deficits.

Non-invasive detection of language-related prefrontal high gamma band activity with beamforming MEG

High gamma band (>50 Hz) activity is a key oscillatory phenomenon of brain activation. However, there has not been a non-invasive method established to detect language-related high gamma band activity. We used a 160-channel whole-head magnetoencephalography (MEG) system equipped with superconducting quantum interference device (SQUID) gradiometers to non-invasively investigate neuromagnetic activities during silent reading and verb generation tasks in 15 healthy participants. Individual data were divided into alpha (8–13 Hz), beta (13–25 Hz), low gamma (25–50 Hz), and high gamma (50–100 Hz) bands and analysed with the beamformer method. The time window was consecutively moved. Group analysis was performed to delineate common areas of brain activation. In the verb generation task, transient power increases in the high gamma band appeared in the left middle frontal gyrus (MFG) at the 550–750 ms post-stimulus window. We set a virtual sensor on the left MFG for time-frequency analysis, and high gamma event-related synchronization (ERS) induced by a verb generation task was demonstrated at 650 ms. In contrast, ERS in the high gamma band was not detected in the silent reading task. Thus, our study successfully non-invasively measured language-related prefrontal high gamma band activity.

From Trust in Automation to Decision Neuroscience: Applying Cognitive Neuroscience Methods to Understand and Improve Interaction Decisions Involved in Human Automation Interaction

Human automation interaction (HAI) systems have thus far failed to live up to expectations mainly because human users do not always interact with the automation appropriately. Trust in automation (TiA) has been considered a central influence on the way a human user interacts with an automation; if TiA is too high there will be overuse, if TiA is too low there will be disuse. However, even though extensive research into TiA has identified specific HAI behaviors, or trust outcomes, a unique mapping between trust states and trust outcomes has yet to be clearly identified. Interaction behaviors have been intensely studied in the domain of HAI and TiA and this has led to a reframing of the issues of problems with HAI in terms of reliance and compliance. We find the behaviorally defined terms reliance and compliance to be useful in their functionality for application in real-world situations. However, we note that once an inappropriate interaction behavior has occurred it is too late to mitigate it. We therefore take a step back and look at the interaction decision that precedes the behavior. We note that the decision neuroscience community has revealed that decisions are fairly stereotyped processes accompanied by measurable psychophysiological correlates. Two literatures were therefore reviewed. TiA literature was extensively reviewed in order to understand the relationship between TiA and trust outcomes, as well as to identify gaps in current knowledge. We note that an interaction decision precedes an interaction behavior and believe that we can leverage knowledge of the psychophysiological correlates of decisions to improve joint system performance. As we believe that understanding the interaction decision will be critical to the eventual mitigation of inappropriate interaction behavior, we reviewed the decision making literature and provide a synopsis of the state of the art understanding of the decision process from a decision neuroscience perspective. We forward hypotheses based on this understanding that could shape a research path toward the ability to mitigate interaction behavior in the real world.

Cerebellar damage impairs the self-rating of regret feeling in a gambling task

Anatomical, clinical, and neuroimaging evidence implicates the cerebellum in processing emotions and feelings. Moreover recent studies showed a cerebellar involvement in pathologies such as autism, schizophrenia and alexithymia, in which emotional processing have been found altered. However, cerebellar function in the modulation of emotional responses remains debated. In this study, emotions that are involved directly in decision-making were examined in 15 patients (six males; age range 17–60 years) affected by cerebellar damage and 15 well matched healthy controls. We used a gambling task, in which subjects’ choices and evaluation of outcomes with regard to their anticipated and actual emotional impact were analyzed. Emotions, such as regret and relief, were elicited, based on the outcome of the unselected gamble. Interestingly, despite their ability to avoid regret in subsequent choices, patients affected by cerebellar lesions were significantly impaired in evaluating the feeling of regret subjectively. These results demonstrate that the cerebellum is involved in conscious recognizing of negative feelings caused by the sense of self-responsibility for an incorrect decision.

The contribution of pre-stimulus neural oscillatory activity to spontaneous response time variability

Large variability between individual response times, even in identical conditions, is a ubiquitous property of animal behavior. However, the origins of this stochasticity and its relation to action decisions remain unclear. Here we focus on the state of the perception-action network in the pre-stimulus period and its influence on subsequent saccadic response time and choice in humans. We employ magnetoencephalography (MEG) and a correlational source reconstruction approach to identify the brain areas where pre-stimulus oscillatory activity predicted saccadic response time to visual targets. We find a relationship between future response time and pre-stimulus power, but not phase, in occipital (including V1), parietal, posterior cingulate and superior frontal cortices, consistently across alpha, beta and low gamma frequencies, each accounting for between 1 and 4% of the RT variance. Importantly, these correlations were not explained by deterministic sources of variance, such as experimental factors and trial history. Our results further suggest that occipital areas mainly reflect short-term (trial to trial) stochastic fluctuations, while the frontal contribution largely reflects longer-term effects such as fatigue or practice. Parietal areas reflect fluctuations at both time scales. We found no evidence of lateralization: these effects were indistinguishable in both hemispheres and for both saccade directions, and non-predictive of choice - a finding with fundamental consequences for models of action decision, where independent, not coupled, noise is normally assumed.

Gamma deficits as a neural signature of cognitive impairment in children treated for brain tumors

Cognitive impairment is consistently reported in children treated for brain tumors, particularly in the categories of processing speed, memory, and attention. Although tumor site, hydrocephalus, chemotherapy, and cranial radiation therapy (CRT) are all associated with poorer function, CRT predicts the greatest deficits. There is a particularly high correlation between CRT and slowed information-processing speed. Cortical gamma-band oscillations have been associated with processing behaviorally relevant information; however, their role in the maintenance of cognition in individuals with processing deficits is unclear. We examined gamma oscillations using magnetoencephalography (MEG) in children undergoing CRT to test whether gamma characteristics can be a signature of cognitive impairment in this population. We collected resting-state data as well as data from baseline and active periods during two visual-motor reaction time tasks of varying cognitive loads from 18 healthy children and 20 patients. We found that only high-gamma oscillations (60-100 Hz), and not low-gamma oscillations (30-59 Hz), showed significant group differences in absolute power levels. Overall, compared with healthy children, patients showed the following: (1) lower total high-gamma (60-100 Hz) power during the resting state, as well as during task-related baseline and performance measures; (2) no change in gamma reactivity to increases in cognitive load; and (3) slower processing speeds both inside and outside MEG. Our findings show that high-gamma oscillations are disrupted in children after treatment for a brain tumor. The temporal dynamic of the high-gamma response during information processing may index cognitive impairment in humans with neurological injury.

Accumulated source imaging of brain activity with both low and high-frequency neuromagnetic signals

Recent studies have revealed the importance of high-frequency brain signals (>70 Hz). One challenge of high-frequency signal analysis is that the size of time-frequency representation of high-frequency brain signals could be larger than 1 terabytes (TB), which is beyond the upper limits of a typical computer workstation's memory (<196 GB). The aim of the present study is to develop a new method to provide greater sensitivity in detecting high-frequency magnetoencephalography (MEG) signals in a single automated and versatile interface, rather than the more traditional, time-intensive visual inspection methods, which may take up to several days. To address the aim, we developed a new method, accumulated source imaging, defined as the volumetric summation of source activity over a period of time. This method analyzes signals in both low- (1~70 Hz) and high-frequency (70~200 Hz) ranges at source levels. To extract meaningful information from MEG signals at sensor space, the signals were decomposed to channel-cross-channel matrix (CxC) representing the spatiotemporal patterns of every possible sensor-pair. A new algorithm was developed and tested by calculating the optimal CxC and source location-orientation weights for volumetric source imaging, thereby minimizing multi-source interference and reducing computational cost. The new method was implemented in C/C++ and tested with MEG data recorded from clinical epilepsy patients. The results of experimental data demonstrated that accumulated source imaging could effectively summarize and visualize MEG recordings within 12.7 h by using approximately 10 GB of computer memory. In contrast to the conventional method of visually identifying multi-frequency epileptic activities that traditionally took 2–3 days and used 1–2 TB storage, the new approach can quantify epileptic abnormalities in both low- and high-frequency ranges at source levels, using much less time and computer memory.

The dual facet of gamma oscillations: separate visual and decision making circuits as revealed by simultaneous EEG/fMRI

It remains an outstanding question whether gamma-band oscillations reflect unitary cognitive processes within the same task. EEG/MEG studies do lack the resolution or coverage to address the highly debated question whether single gamma activity patterns are linked with multiple cognitive modules or alternatively each pattern associates with a specific cognitive module, within the same coherent perceptual task. One way to disentangle these issues would be to provide direct identification of their sources, by combining different techniques. Here, we directly examined these questions by performing simultaneous EEG/fMRI using an ambiguous perception paradigm requiring holistic integration. We found that distinct gamma frequency sub-bands reflect different neural substrates and cognitive mechanisms when comparing object perception states vs. no categorical perception. A low gamma sub-band (near 40 Hz) activity was tightly related to the decision making network, and in particular the anterior insula. A high gamma sub-band (∼60 Hz) could be linked to early visual processing regions. The demonstration of a clear functional topography for distinct gamma sub-bands within the same task shows that distinct gamma-band modulations underlie sensory processing and perceptual decision mechanisms.

Timing and awareness of movement decisions: does consciousness really come too late?

Since Libet's seminal observation that a brain potential related to movement preparation occurs before participants report to be aware of their movement intention, it has been debated whether consciousness has causal influence on movement decisions. Here we review recent advances that provide new insights into the dynamics of human decision-making and question the validity of different markers used for determining the onset of neural and conscious events. Motor decisions involve multiple stages of goal evaluation, intention formation, and action execution. While the validity of the Bereitschaftspotential (BP) as index of neural movement preparation is controversial, improved neural markers are able to predict decision outcome even at early stages. Participants report being conscious of their decisions only at the time of final intention formation, just before the primary motor cortex starts executing the chosen action. However, accumulating evidence suggests that this is an artifact of Libet's clock method used for assessing consciousness. More refined methods suggest that intention consciousness does not appear instantaneously but builds up progressively. In this view, early neural markers of decision outcome are not unconscious but simply reflect conscious goal evaluation stages which are not final yet and therefore not reported with the clock method. Alternatives to the Libet clock are discussed that might allow for assessment of consciousness during decision making with improved sensitivity to early decision stages and with less influence from meta-conscious and perceptual inferences.

Neural changes with tactile learning reflect decision-level reweighting of perceptual readout

Despite considerable work, the neural basis of perceptual learning remains uncertain. For visual learning, although some studies suggested that changes in early sensory representations are responsible, other studies point to decision-level reweighting of perceptual readout. These competing possibilities have not been examined in other sensory systems, investigating which could help resolve the issue. Here we report a study of human tactile microspatial learning in which participants achieved >six-fold decline in acuity threshold after multiple training sessions. Functional magnetic resonance imaging was performed during performance of the tactile microspatial task and a control, tactile temporal task. Effective connectivity between relevant brain regions was estimated using multivariate, autoregressive models of hidden neuronal variables obtained by deconvolution of the hemodynamic response. Training-specific increases in task-selective activation assessed using the task × session interaction and associated changes in effective connectivity primarily involved subcortical and anterior neocortical regions implicated in motor and/or decision processes, rather than somatosensory cortical regions. A control group of participants tested twice, without intervening training, exhibited neither threshold improvement nor increases in task-selective activation. Our observations argue that neuroplasticity mediating perceptual learning occurs at the stage of perceptual readout by decision networks. This is consonant with the growing shift away from strictly modular conceptualization of the brain toward the idea that complex network interactions underlie even simple tasks. The convergence of our findings on tactile learning with recent studies of visual learning reconciles earlier discrepancies in the literature on perceptual learning.

Deficits in high- (>60 Hz) gamma-band oscillations during visual processing in schizophrenia

Current theories of the pathophysiology of schizophrenia have focused on abnormal temporal coordination of neural activity. Oscillations in the gamma-band range (>25 Hz) are of particular interest as they establish synchronization with great precision in local cortical networks. However, the contribution of high gamma (>60 Hz) oscillations toward the pathophysiology is less established. To address this issue, we recorded magnetoencephalographic (MEG) data from 16 medicated patients with chronic schizophrenia and 16 controls during the perception of Mooney faces. MEG data were analysed in the 25–150 Hz frequency range. Patients showed elevated reaction times and reduced detection rates during the perception of upright Mooney faces while responses to inverted stimuli were intact. Impaired processing of Mooney faces in schizophrenia patients was accompanied by a pronounced reduction in spectral power between 60–120 Hz (effect size: d = 1.26) which was correlated with disorganized symptoms (r = −0.72). Our findings demonstrate that deficits in high gamma-band oscillations as measured by MEG are a sensitive marker for aberrant cortical functioning in schizophrenia, suggesting an important aspect of the pathophysiology of the disorder.

Seeking a unified framework for cerebellar function and dysfunction: from circuit operations to cognition

Following the fundamental recognition of its involvement in sensory-motor coordination and learning, the cerebellum is now also believed to take part in the processing of cognition and emotion. This hypothesis is recurrent in numerous papers reporting anatomical and functional observations, and it requires an explanation. We argue that a similar circuit structure in all cerebellar areas may carry out various operations using a common computational scheme. On the basis of a broad review of anatomical data, it is conceivable that the different roles of the cerebellum lie in the specific connectivity of the cerebellar modules, with motor, cognitive, and emotional functions (at least partially) segregated into different cerebro-cerebellar loops. We here develop a conceptual and operational framework based on multiple interconnected levels (a meta-levels hypothesis): from cellular/molecular to network mechanisms leading to generation of computational primitives, thence to high-level cognitive/emotional processing, and finally to the sphere of mental function and dysfunction. The main concept explored is that of intimate interplay between timing and learning (reminiscent of the “timing and learning machine” capabilities long attributed to the cerebellum), which reverberates from cellular to circuit mechanisms. Subsequently, integration within large-scale brain loops could generate the disparate cognitive/emotional and mental functions in which the cerebellum has been implicated. We propose, therefore, that the cerebellum operates as a general-purpose co-processor, whose effects depend on the specific brain centers to which individual modules are connected. Abnormal functioning in these loops could eventually contribute to the pathogenesis of major brain pathologies including not just ataxia but also dyslexia, autism, schizophrenia, and depression.

Aberrant Oscillatory Activity during Simple Movement in Task-Specific Focal Hand Dystonia

In task-specific focal hand dystonia (tspFHD), the temporal dynamics of cortical activity in the motor system and how these processes are related to impairments in sensory and motor function are poorly understood. Here, we use time-frequency reconstructions of magnetoencephalographic (MEG) data to elaborate the temporal and spatial characteristics of cortical activity during movement. A self-paced finger tapping task during MEG recording was performed by 11 patients with tspFHD and 11 matched healthy controls. In both groups robust changes in beta (12-30 Hz) and high gamma (65-90 Hz) oscillatory activity were identified over sensory and motor cortices during button press. A significant decrease [p < 0.05, 1% False Discovery Rate (FDR) corrected] in high gamma power during movements of the affected hand was identified over ipsilateral sensorimotor cortex in the period prior to (-575 ms) and following (725 ms) button press. Furthermore, an increase (p < 0.05, 1% FDR corrected) in beta power suppression following movement of the affected hand was identified over visual cortex in patients with tspFHD. For movements of the unaffected hand, a significant (p < 0.05, 1% FDR corrected) increase in beta power suppression was identified over secondary somatosensory cortex (S2) in the period following button press in patients with tspFHD. Oscillatory activity within in the tspFHD group was however not correlated with clinical measures. Understanding these aberrant oscillatory dynamics can provide the groundwork for interventions that focus on modulating the timing of this activity.

Dynamics of hemispheric dominance for language assessed by magnetoencephalographic imaging

OBJECTIVE

The goal of the current study was to examine the dynamics of language lateralization using magnetoencephalographic (MEG) imaging, to determine the sensitivity and specificity of MEG imaging, and to determine whether MEG imaging can become a viable alternative to the intracarotid amobarbital procedure (IAP), the current gold standard for preoperative language lateralization in neurosurgical candidates.

METHODS

MEG was recorded during an auditory verb generation task and imaging analysis of oscillatory activity was initially performed in 21 subjects with epilepsy, brain tumor, or arteriovenous malformation who had undergone IAP and MEG. Time windows and brain regions of interest that best discriminated between IAP-determined left or right dominance for language were identified. Parameters derived in the retrospective analysis were applied to a prospective cohort of 14 patients and healthy controls.

RESULTS

Power decreases in the beta frequency band were consistently observed following auditory stimulation in inferior frontal, superior temporal, and parietal cortices; similar power decreases were also seen in inferior frontal cortex prior to and during overt verb generation. Language lateralization was clearly observed to be a dynamic process that is bilateral for several hundred milliseconds during periods of auditory perception and overt speech production. Correlation with the IAP was seen in 13 of 14 (93%) prospective patients, with the test demonstrating a sensitivity of 100% and specificity of 92%.

INTERPRETATION

Our results demonstrate excellent correlation between MEG imaging findings and the IAP for language lateralization, and provide new insights into the spatiotemporal dynamics of cortical speech processing.

Consensus paper: roles of the cerebellum in motor control--the diversity of ideas on cerebellar involvement in movement

Considerable progress has been made in developing models of cerebellar function in sensorimotor control, as well as in identifying key problems that are the focus of current investigation. In this consensus paper, we discuss the literature on the role of the cerebellar circuitry in motor control, bringing together a range of different viewpoints. The following topics are covered: oculomotor control, classical conditioning (evidence in animals and in humans), cerebellar control of motor speech, control of grip forces, control of voluntary limb movements, timing, sensorimotor synchronization, control of corticomotor excitability, control of movement-related sensory data acquisition, cerebro-cerebellar interaction in visuokinesthetic perception of hand movement, functional neuroimaging studies, and magnetoencephalographic mapping of cortico-cerebellar dynamics. While the field has yet to reach a consensus on the precise role played by the cerebellum in movement control, the literature has witnessed the emergence of broad proposals that address cerebellar function at multiple levels of analysis. This paper highlights the diversity of current opinion, providing a framework for debate and discussion on the role of this quintessential vertebrate structure.

The neural basis of event-time introspection

We explored the neural mechanisms allowing humans to report the subjective onset times of conscious events. Magnetoencephalographic recordings of neural oscillations were obtained while human subjects introspected the timing of sensory, intentional, and motor events during a forced choice task. Brain activity was reconstructed with high spatio-temporal resolution. Event-time introspection was associated with specific neural activity at the time of subjective event onset which was spatially distinct from activity induced by the event itself. Different brain regions were selectively recruited for introspection of different event types, e.g., the bilateral angular gyrus for introspection of intention. Our results suggest that event-time introspection engages specific neural networks to assess the contents of consciousness. Subjective event times should therefore be interpreted as the result of complex interactions between introspection and experience networks, rather than as direct reproduction of the individual's conscious state or as a mere post hoc interpretation.

MEG/EEG source reconstruction, statistical evaluation, and visualization with NUTMEG

NUTMEG is a source analysis toolbox geared towards cognitive neuroscience researchers using MEG and EEG, including intracranial recordings. Evoked and unaveraged data can be imported to the toolbox for source analysis in either the time or time-frequency domains. NUTMEG offers several variants of adaptive beamformers, probabilistic reconstruction algorithms, as well as minimum-norm techniques to generate functional maps of spatiotemporal neural source activity. Lead fields can be calculated from single and overlapping sphere head models or imported from other software. Group averages and statistics can be calculated as well. In addition to data analysis tools, NUTMEG provides a unique and intuitive graphical interface for visualization of results. Source analyses can be superimposed onto a structural MRI or headshape to provide a convenient visual correspondence to anatomy. These results can also be navigated interactively, with the spatial maps and source time series or spectrogram linked accordingly. Animations can be generated to view the evolution of neural activity over time. NUTMEG can also display brain renderings and perform spatial normalization of functional maps using SPM's engine. As a MATLAB package, the end user may easily link with other toolboxes or add customized functions.

Cortical temporal dynamics of visually guided behavior

Little is known about the temporal dynamics of cortical activation during visually guided behavior. We measured changes in brain activity in human posterior parietal cortex (PPC) and premotor cortex (PMC) during saccades and visually guided reaching using magnetoencephalography (MEG) and novel time-frequency reconstructions of MEG (tfMEG) data. Results indicate that early high-gamma activity over the frontal eye fields (FEFs) was present during saccade preparation, and high-gamma activity progressed from the supplementary and FEFs to visual cortex during saccade execution. In contrast, early high-gamma activity over dorsal PMC and late beta activity in primary motor cortex and PPC were unique to reach preparation. During reaching, high-gamma activity progressed from sensorimotor cortex and PMC to parietooccipital cortex. These unique spatial-temporal processing patterns reflect the known connectivity of 2 different sensorimotor networks in macaques. The onset and duration of activity in these areas provides direct evidence for concurrent serial and parallel processing in the human brain during the integration of the sensorimotor inputs necessary for visually guided performance.

Neuroelectromagnetic correlates of perceptual closure processes

Perceptual closure refers to the coherent perception of an object under circumstances when the visual information is incomplete. Although the perceptual closure index observed in electroencephalography reflects that an object has been recognized, the full spatiotemporal dynamics of cortical source activity underlying perceptual closure processing remain unknown so far. To address this question, we recorded magnetoencephalographic activity in 15 subjects (11 females) during a visual closure task and performed beamforming over a sequence of successive short time windows to localize high-frequency gamma-band activity (60-100 Hz). Two-tone images of human faces (Mooney faces) were used to examine perceptual closure. Event-related fields exhibited a magnetic closure index between 250 and 325 ms. Time-frequency analyses revealed sustained high-frequency gamma-band activity associated with the processing of Mooney stimuli; closure-related gamma-band activity was observed between 200 and 300 ms over occipitotemporal channels. Time-resolved source reconstruction revealed an early (0-200 ms) coactivation of caudal inferior temporal gyrus (cITG) and regions in posterior parietal cortex (PPC). At the time of perceptual closure (200-400 ms), the activation in cITG extended to the fusiform gyrus, if a face was perceived. Our data provide the first electrophysiological evidence that perceptual closure for Mooney faces starts with an interaction between areas related to processing of three-dimensional structure from shading cues (cITG) and areas associated with the activation of long-term memory templates (PPC). Later, at the moment of perceptual closure, inferior temporal cortex areas specialized for the perceived object are activated, i.e., the fusiform gyrus related to face processing for Mooney stimuli.

Synchronous with your feelings: sensorimotor {gamma} band and empathy for pain

Neuroscience studies on the social sharing of observed or imagined pain focused on whether empathic pain resonance is linked to affective or sensory nodes of the pain matrix. However, empathy, like other complex cognitive processes, is inherently linked to the activation of functional networks rather than of separate brain areas. Here, we used magnetoencephalography (MEG) to explore the relationship between empathy and functional coupling of neuronal activity in primary somatosensory (SI) and motor (MI) cortices. MEG recording was performed while healthy participants observed movie-clips depicting the static hand of a stranger model, the same hand deeply penetrated by a needle, or gently touched by a Q-tip. Subjects were asked to rate the movie-derived sensations attributed to self or to the model. For each type of clip observation, we analyzed spectral power and coherence values in alpha, beta, and gamma frequency bands. While spectral power indexes separate neural activity in SI and MI, coherence values index functional cross-talk between these two areas. No power changes of SI or MI sources were induced by observation conditions in any of the frequency bands. Crucially, gamma-band coherence values were significantly higher during needle-in-hand than touch and static hand observation and correlated with self-and other-referred pain ratings derived from needle-in-hand movies observation. Thus, observation of others' pain increases neuronal synchronization and cross-talk between the onlookers' sensory and motor cortices, indicating that empathic resonance relies upon the activity of functional networks more than of single areas.

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.

Five-dimensional neuroimaging: localization of the time-frequency dynamics of cortical activity

The spatiotemporal dynamics of cortical oscillations across human brain regions remain poorly understood because of a lack of adequately validated methods for reconstructing such activity from noninvasive electrophysiological data. In this paper, we present a novel adaptive spatial filtering algorithm optimized for robust source time-frequency reconstruction from magnetoencephalography (MEG) and electroencephalography (EEG) data. The efficacy of the method is demonstrated with simulated sources and is also applied to real MEG data from a self-paced finger movement task. The algorithm reliably reveals modulations both in the beta band (12-30 Hz) and high gamma band (65-90 Hz) in sensorimotor cortex. The performance is validated by both across-subjects statistical comparisons and by intracranial electrocorticography (ECoG) data from two epilepsy patients. Interestingly, we also reliably observed high frequency activity (30-300 Hz) in the cerebellum, although with variable locations and frequencies across subjects. The proposed algorithm is highly parallelizable and runs efficiently on modern high-performance computing clusters. This method enables the ultimate promise of MEG and EEG for five-dimensional imaging of space, time, and frequency activity in the brain and renders it applicable for widespread studies of human cortical dynamics during cognition.