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When we fixate an object, visual information is continuously received on the retina. Several studies observed behavioral oscillations in perceptual sensitivity across such stimulus time, and these fluctuations have been linked to brain oscillations. However, whether specific brain areas show oscillations across stimulus time (i.e., different time points of the stimulus being more or less processed, in a rhythmic fashion) has not been investigated. Here, we revealed random areas of face images at random moments across time and recorded the brain activity of male and female human participants using MEG while they performed two recognition tasks. This allowed us to quantify how each snapshot of visual information coming from the stimulus is processed across time and across the brain. Oscillations across stimulus time (rhythmic sampling) were mostly visible in early visual areas, at theta, alpha, and low beta frequencies. We also found that they contributed to brain activity more than previously investigated rhythmic processing (oscillations in the processing of a single snapshot of visual information). Nonrhythmic sampling was also visible at later latencies across the visual cortex, either in the form of a transient processing of early stimulus time points or of a sustained processing of the whole stimulus. Our results suggest that successive cycles of ongoing brain oscillations process stimulus information incoming at successive moments. Together, these results advance our understanding of the oscillatory neural dynamics associated with visual processing and show the importance of considering the temporal dimension of stimuli when studying visual recognition. [ABSTRACT FROM AUTHOR]

Criticality is the singular state of complex systems poised at the brink of a phase transition between order and randomness. Such systems display remarkable information-processing capabilities, evoking the compelling hypothesis that the brain may itself be critical. This foundational idea is now drawing renewed interest thanks to high-density data and converging cross-disciplinary knowledge. Together, these lines of inquiry have shed light on the intimate link between criticality, computation, and cognition. Here, we review these emerging trends in criticality neuroscience, highlighting new data pertaining to the edge of chaos and near-criticality, and making a case for the distance to criticality as a useful metric for probing cognitive states and mental illness. This unfolding progress in the field contributes to establishing criticality theory as a powerful mechanistic framework for studying emergent function and its efficiency in both biological and artificial neural networks. Empirical and theoretical work suggests that the brain operates at the edge of a critical phase transition between order and disorder. The wider adoption and investigation of criticality theory as a unifying framework in neuroscience has been hindered in part by the potentially daunting complexity of its analytical and theoretical foundation. Among critical phase transitions, avalanche and edge of chaos criticality are particularly relevant to studying brain function and dysfunction. The computational features of criticality provide a conceptual link between neuronal dynamics and cognition. Mounting evidence suggests that near-criticality, more than strict criticality, may be a more plausible mode of operation for the brain. The distance to criticality presents a promising and underexploited biological parameter for characterizing cognitive differences and mental illness. [ABSTRACT FROM AUTHOR]

Machine learning techniques are increasingly used in neuroscience to classify brain signals. Decoding performance is reflected by how much the classification results depart from the rate achieved by purely random classification. In a 2-class or 4-class classification problem, the chance levels are thus 50% or 25% respectively. However, such thresholds hold for an infinite number of data samples but not for small data sets. While this limitation is widely recognized in the machine learning field, it is unfortunately sometimes still overlooked or ignored in the emerging field of brain signal classification. Incidentally, this field is often faced with the difficulty of low sample size. In this study we demonstrate how applying signal classification to Gaussian random signals can yield decoding accuracies of up to 70% or higher in two-class decoding with small sample sets. Most importantly, we provide a thorough quantification of the severity and the parameters affecting this limitation using simulations in which we manipulate sample size, class number, cross-validation parameters ( k -fold, leave-one-out and repetition number) and classifier type (Linear-Discriminant Analysis, Naïve Bayesian and Support Vector Machine). In addition to raising a red flag of caution, we illustrate the use of analytical and empirical solutions (binomial formula and permutation tests) that tackle the problem by providing statistical significance levels ( p -values) for the decoding accuracy, taking sample size into account. Finally, we illustrate the relevance of our simulations and statistical tests on real brain data by assessing noise-level classifications in Magnetoencephalography (MEG) and intracranial EEG (iEEG) baseline recordings. [ABSTRACT FROM AUTHOR]

Visuospatial attention is not a monolithic process and can be divided into different functional systems. In this framework, exogenous attention reflects the involuntary orienting of attention resources following a salient event, whereas endogenous attention corresponds to voluntary orienting based on the goals and intentions of individuals. Previous work shows that these attention processes map onto distinct functional systems, yet evidence suggests that they are not fully independent. In the current work, we investigated the differential and overlapping effects of exogenous and endogenous attention on visual processing. We combined spatial cueing of visuospatial attention, EEG, and multivariate pattern analysis to examine where and when the effects of exogenous and endogenous attention were maximally different and maximally similar. Critically, multivariate pattern analysis provided new insights by examining whether classifiers trained to decode the cueing effect for one attention process (e.g., exogenous attention) can successfully decode the cueing effect for the other attention process (e.g., endogenous attention). These analyses uncovered differential and overlapping effects between exogenous and endogenous attention. Next, we combined principal component analyses, single-trial ERPs, and mediation analysis to determine whether these effects facilitate perception, as indexed by the behavioral spatial cueing effects of exogenous and endogenous attention. This approach revealed that three EEG components shape the cueing effects of exogenous and endogenous attention at various times after target onset. Altogether, our study provides a comprehensive account about how overlapping and differential processes of endogenous and exogenous relate to perceptual facilitation in the context of visuospatial attention. [ABSTRACT FROM AUTHOR]

Recognition of social hierarchy is a key feature that helps us navigate through our complex social environment. Neuroimaging studies have identified brain structures involved in the processing of hierarchical stimuli but the precise temporal dynamics of brain activity associated with such processing remains largely unknown. Here, we used electroencephalography to examine the effect of social hierarchy on neural responses elicited by faces. In contrast to previous studies, the key manipulation was that a hierarchical context was constructed, not by varying facial expressions, but by presenting neutral-expression faces in a game setting. Once the performance-based hierarchy was established, participants were presented with high-rank, middle-rank and low-rank player faces and had to evaluate the rank of each face with respect to their own position. Both event-related potentials and task-related oscillatory activity were investigated. Three main findings emerge from the study. First, the experimental manipulation had no effect on the early N170 component, which may suggest that hierarchy did not modulate the structural encoding of neutral-expression faces. Second, hierarchy significantly modulated the amplitude of the late positive potential (LPP) within a 400–700 ms time-window, with more a prominent LPP occurring when the participants processed the face of the highest-rank player. Third, high-rank faces were associated with the highest reduction of alpha power. Taken together these findings provide novel electrophysiological evidence for enhanced allocation of attentional resource in the presence of high-rank faces. At a broader level, this study brings new insights into the neural processing underlying social categorization. [ABSTRACT FROM AUTHOR]

Task performance is associated with increased brain metabolism but also with prominent deactivation in specific brain structures known as the default-mode network (DMN). The role ofDMNdeactivation remains enigmatic in part because its electrophysiological correlates, temporal dynamics, and link to behavior are poorly understood. Using extensive depth electrode recordings in humans, we provide first electrophysiological evidence for a direct correlation between the dynamics of power decreases in the DMN and individual subject behavior. We found that all DMN areas displayed transient suppressions of broadband gamma (60 -140 Hz) power during performance of a visual search task and, critically, we show for the first time that the millisecond range duration and extent of the transient gamma suppressions are correlated with task complexity and subject performance. In addition, trial-by-trial correlations revealed that spatially distributed gamma power increases and decreases formed distinct anticorrelated large-scale networks. Beyond unraveling the electrophysiological basis ofDMNdynamics, our results suggest that, rather than indicating a mere switch to a global exteroceptive mode,DMN deactivation encodes the extent and efficiency of our engagement with the external world. Furthermore, our findings reveal a pivotal role for broadband gamma modulations in the interplay between task-positive and task-negative networks mediating efficient goal-directed behavior and facilitate our understanding of the relationship between electrophysiology and neuroimaging studies of intrinsic brain networks. [ABSTRACT FROM AUTHOR]

The recent surge in computational power has led to extensive methodological developments and advanced signal processing techniques that play a pivotal role in neuroscience. In particular, the field of brain signal analysis has witnessed a strong trend towards multidimensional analysis of large data sets, for example, single-trial time-frequency analysis of high spatiotemporal resolution recordings. Here, we describe the freely available ELAN software package which provides a wide range of signal analysis tools for electrophysiological data including scalp electroencephalography (EEG), magnetoencephalography (MEG), intracranial EEG, and local field potentials (LFPs). The ELAN toolbox is based on 25 years of methodological developments at the Brain Dynamics and Cognition Laboratory in Lyon and was used in many papers including the very first studies of time-frequency analysis of EEG data exploring evoked and induced oscillatory activities in humans. This paper provides an overview of the concepts and functionalities of ELAN, highlights its specificities, and describes its complementarity and interoperability with other toolboxes. [ABSTRACT FROM AUTHOR]

Evaluating the outcome of our own actions is a fundamental process by which we adapt our behavior in our interaction with the external world. fMRI and electrophysiological studies in monkeys have found feedback-specific responses in several brain regions, unveiling facets of a large-scale network predominantly distributed in the frontal lobes. However, a consensus has yet to be reached regarding the exact contribution of each region. The present study benefited from intracerebral EEG recordings in epileptic patients to record directly the neural activity in each of those frontal structures in response to positive and negative feedback. Both types of feedback induced a sequence of high-frequency responses (>40 Hz) in a widespread network involving medial frontal cortex, dorsolateral prefrontal cortex (DLPFC), orbitofrontal cortex (OFC), and insular cortex. The pre-supplementary motor area (pre-SMA), DLPFC, and lateral OFC showed higher activation in response to negative feedback, while medial OFC and dorsal anterior cingulate cortex (dACC) were more responsive to positive feedback. Responses in the medial prefrontal cortex (pre-SMA and dACC) were sustained (lasting more than 1,000 ms), while responses in the DLPFC, insula, and the OFC were short lasting (less than 800 ms). Taken together, our findings show that evaluating the outcome of our actions triggers γ-range activity modulations in several frontal and insular regions. Moreover, we found that the timing and amplitude of those γ-band responses reveal fine-scale dissociations between the neural dynamics of positive versus negative feedback processing. Hum Brain Mapp, 2010. © 2010 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

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. Hum Brain Mapp, 2009. © 2009 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Abstract: Imaging neural generators from MEG magnetic fields is often considered as a compromise between computationally-reasonable methodology that usually yields poor spatial resolution on the one hand, and more sophisticated approaches on the other hand, potentially leading to intractable computational costs. We approach the problem of obtaining well-resolved source images with unexcessive computation load with a multiresolution image model selection (MiMS) technique. The building blocks of the MiMS source model are parcels of the cortical surface which can be designed at multiple spatial resolutions with the combination of anatomical and functional priors. Computation charge is reduced owing to 1) compact parametric models of the activation of extended brain parcels using current multipole expansions and 2) the optimization of the generalized cross-validation error on image models, which is closed-form for the broad class of linear estimators of neural currents. Model selection can be complemented by any conventional imaging approach of neural currents restricted to the optimal image support obtained from MiMS. The estimation of the location and spatial extent of brain activations is discussed and evaluated using extensive Monte-Carlo simulations. An experimental evaluation was conducted with MEG data from a somatotopic paradigm. Results show that MiMS is an efficient image model selection technique with robust performances at realistic noise levels. [Copyright &y& Elsevier]

The spiking activity of single neurons in the primate motor cortex is correlated with various limb movement parameters, including velocity. Recent findings obtained using local field potentials suggest that hand speed may also be encoded in the summed activity of neuronal populations. At this macroscopic level, the motor cortex has also been shown to display synchronized rhythmic activity modulated by motor behavior. Yet whether and how neural oscillations might be related to limb speed control is still poorly understood. Here, we applied magnetoencephalography (MEG) source imaging to the ongoing brain activity in subjects performing a continuous visuomotor (VM) task. We used coherence and phase synchronization to investigate the coupling between the estimated activity throughout the brain and the simultaneously recorded instantaneous hand speed. We found significant phase locking between slow (2- to 5-Hz) oscillatory activity in the contralateral primary motor cortex and time-varying hand speed. In addition, we report long-range task-related coupling between primary motor cortex and multiple brain regions in the same frequency band. The detected large-scale VM network spans several cortical and subcortical areas, including structures of the frontoparietal circuit and the cerebello-thalamo-cortical pathway. These findings suggest a role for slow coherent oscillations in mediating neural representations of hand kinematics in humans and provide further support for the putative role of long-range neural synchronization in large-scale VM integration. Our findings are discussed in the context of corticomotor communication, distributed motor encoding, and possible implications for brain-machine interfaces. [ABSTRACT FROM AUTHOR]

Hypnotic phenomena exhibit significant inter-individual variability, with some individuals consistently demonstrating efficient responses to hypnotic suggestions, while others show limited susceptibility. Recent neurophysiological studies have added to a growing body of research that shows variability in hypnotic susceptibility is linked to distinct neural characteristics. Building on this foundation, our previous work identified that individuals with high and low hypnotic susceptibility can be differentiated based on the arrhythmic activity observed in resting-state electrophysiology (rs-EEG) outside of hypnosis. However, because previous work has largely focused on mean spectral characteristics, our understanding of the variability over time of these features, and how they relate to hypnotic susceptibility, is still limited. Here we address this gap using a time-resolved assessment of rhythmic alpha peaks and arrhythmic components of the EEG spectrum both prior to and following hypnotic induction. Using multivariate pattern classification, we investigated whether these neural features differ between individuals with high and low susceptibility to hypnosis. Specifically, we used multivariate pattern classification to investigate whether these non-stationary neural features could distinguish between individuals with high and low susceptibility to hypnosis before and after a hypnotic induction. Our analytical approach focused on time-resolved spectral decomposition to capture the intricate dynamics of neural oscillations and their non-oscillatory counterpart, as well as Lempel–Ziv complexity. Our results show that variations in the alpha center frequency are indicative of hypnotic susceptibility, but this discrimination is only evident during hypnosis. Highly hypnotic-susceptible individuals exhibit higher variability in alpha peak center frequency. These findings underscore how dynamic changes in neural states related to alpha peak frequency represent a central neurophysiological feature of hypnosis and hypnotic susceptibility. [ABSTRACT FROM AUTHOR]

The article comments on the functional magnetic resonance imaging research about the components of the cerebral network mediating saccade preparation in humans. The authors discuss the issues affecting the interpretation of the findings of the research by Van Der Werf and colleagues on the delayed saccade paradigm. A review of studies stating the many variants of the delayed saccade task is presented.

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. • Electro- and magnetoencephalography can detect cerebellar signals. • We propose guidelines for detecting cerebellar signals non-invasively. • The cerebellum subserves several functions beyond mere motor processing. [ABSTRACT FROM AUTHOR]

Consciousness has been proposed to be supported by electrophysiological patterns poised at criticality, a dynamical regime which exhibits adaptive computational properties, maximally complex patterns and divergent sensitivity to perturbation. Here, we investigate dynamical properties of the resting-state electroencephalogram (EEG) of healthy subjects undergoing general anesthesia with propofol, xenon or ketamine. Importantly, all participants were unresponsive under anesthesia, while consciousness was retained only during ketamine anesthesia (in the form of vivid dreams), enabling an experimental dissociation between unresponsiveness and unconsciousness. For each condition, we measure (i) avalanche criticality, (ii) chaoticity, and (iii) criticality-related metrics, revealing that states of unconsciousness are characterized by a distancing from both avalanche criticality and the edge of chaos. We then ask whether these same dynamical properties are predictive of the perturbational complexity index (PCI), a TMS-based measure that has shown remarkably high sensitivity in detecting consciousness independently of behavior. We successfully predict individual subjects' PCI values with considerably high accuracy from resting-state EEG dynamical properties alone. Our results establish a firm link between perturbational complexity and criticality, and provide further evidence that criticality is a necessary condition for the emergence of consciousness. An EEG study demonstrates that dynamical properties of resting-state EEG mark (un)consciousness under propofol, xenon and ketamine anesthesia and are able to predict the TMS-derived perturbational complexity index (PCI) in healthy adults. [ABSTRACT FROM AUTHOR]

Limb movement direction can be inferred from local field potentials in motor cortex during movement execution. Yet, it remains unclear to what extent intended hand movements can be predicted from brain activity recorded during movement planning. Here, we set out to probe the directional-tuning of oscillatory features during motor planning and execution, using a machine learning framework on multi-site local field potentials (LFPs) in humans. We recorded intracranial EEG data from implanted epilepsy patients as they performed a four-direction delayed center-out motor task. Fronto-parietal LFP low-frequency power predicted hand-movement direction during planning while execution was largely mediated by higher frequency power and low-frequency phase in motor areas. By contrast, Phase-Amplitude Coupling showed uniform modulations across directions. Finally, multivariate classification led to an increase in overall decoding accuracy (>80%). The novel insights revealed here extend our understanding of the role of neural oscillations in encoding motor plans. A study using intracranial recordings in humans suggests that upcoming hand movements can be predicted by oscillatory brain features of the LFP signal. [ABSTRACT FROM AUTHOR]

Abstract This study examines the influence of social hierarchy on the neural electrophysiological responses to faces. In contrast with earlier EEG studies that typically manipulate social rank through competitive situations, we implemented hierarchy through occupational status and thus contrasted faces associated with high- vs. low-status (e.g. lawyer vs. waiter). Since social hierarchies are largely intertwined with gender, both female and male faces were used as stimuli, and both female and male participants were tested. The procedure consisted in presenting a status label before the face it was associated with. The analyses focused mainly on two components that have been shown to be modulated by competitive hierarchies and other social contexts, namely the N170 and the Late Positive Potential (LPP). The results indicated that gender, but not status, modulated the N170 amplitude. Moreover, high-status faces elicited larger LPP amplitude than low-status faces but this difference was driven by female participants. This gender effect is discussed in line with research showing that women and men are sensitive to different kinds of hierarchy. Methodological differences are considered to account for the discrepancy between studies that find an effect of hierarchy on the N170 and those that do not. Highlights • ERPs elicited by faces associated with distinct occupational statuses and gender. • Face gender, but not status, modulated the N170 component. • High-status faces elicited larger LPP amplitude than low-status faces. • The LPP sensitivity to status was driven by female participants. [ABSTRACT FROM AUTHOR]

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

Videogames are emerging as a promising experimental paradigm in neuroimaging. Acquiring gameplay in a scanner remains challenging due to the lack of a scanner-compatible videogame controller that provides a similar experience to standard, commercial devices. In this paper, we introduce a videogame controller designed for use in the functional magnetic resonance imaging as well as magnetoencephalography. The controller is made exclusively of 3D-printed and commercially available parts. We evaluated the quality of our controller by comparing it to a non-MRI compatible controller that was kept outside the scanner. The comparison of response latencies showed reliable button press accuracies of adequate precision. Comparison of the subjects' motion during fMRI recordings of various tasks showed that the use of our controller did not increase the amount of motion produced compared to a regular MR compatible button press box. Motion levels during an ecological videogame task were of moderate amplitude. In addition, we found that the controller only had marginal effect on temporal SNR in fMRI, as well as on covariance between sensors in MEG, as expected due to the use of non-magnetic building materials. Finally, the reproducibility of the controller was demonstrated by having team members who were not involved in the design build a reproduction using only the documentation. This new videogame controller opens new avenues for ecological tasks in fMRI, including challenging videogames and more generally tasks with complex responses. The detailed controller documentation and build instructions are released under an Open Source Hardware license to increase accessibility, and reproducibility and enable the neuroimaging research community to improve or modify the controller for future experiments. [ABSTRACT FROM AUTHOR]

Self-induced cognitive trance (SICT) is a voluntary non-ordinary state of consciousness characterized by a lucid yet narrowed awareness of the external surroundings. It involves a hyper-focused immersive experience of flow, expanded inner imagery, modified somatosensory processing, and an altered perception of self and time. SICT is gaining attention due to its potential clinical applications. Similar states of non-ordinary state of consciousness, such as meditation, hypnosis, and psychedelic experiences, have been reported to induce changes in the autonomic nervous system. However, the functioning of the autonomic nervous system during SICT remains poorly understood. In this study, we aimed to investigate the impact of SICT on the cardiac and respiratory signals of 25 participants proficient in SICT. To accomplish this, we measured various metrics of heart rate variability (HRV) and respiration rate variability (RRV) in three conditions: resting state, SICT, and a mental imagery task. Subsequently, we employed a machine learning framework utilizing a linear discriminant analysis classifier and a cross-validation scheme to identify the features that exhibited the best discrimination between these three conditions. The results revealed that during SICT, participants experienced an increased heart rate and a decreased level of high-frequency (HF) HRV compared to the control conditions. Additionally, specific increases in respiratory amplitude, phase ratio, and RRV were observed during SICT in comparison to the other conditions. These findings suggest that SICT is associated with a reduction in parasympathetic activity, indicative of a hyperarousal state of the autonomic nervous system during SICT. [ABSTRACT FROM AUTHOR]

Visuospatial attention can be deployed to different locations in space independently of ocular fixation, and studies have shown that event-related potential (ERP) components can effectively index whether such covert visuospatial attention is deployed to the left or right visual field. However, it is not clear whether we may obtain a more precise spatial localization of the focus of attention based on the EEG signals during central fixation. In this study, we used a modified Posner cueing task with an endogenous cue to determine the degree to which information in the EEG signal can be used to track visual spatial attention in presentation sequences lasting 200 ms. We used a machine learning classification method to evaluate how well EEG signals discriminate between four different locations of the focus of attention. We then used a multi-class support vector machine (SVM) and a leave-one-out cross-validation framework to evaluate the decoding accuracy (DA). We found that ERP-based features from occipital and parietal regions showed a statistically significant valid prediction of the location of the focus of visuospatial attention (DA = 57%, p < .001, chance-level 25%). The mean distance between the predicted and the true focus of attention was 0.62 letter positions, which represented a mean error of 0.55 degrees of visual angle. In addition, ERP responses also successfully predicted whether spatial attention was allocated or not to a given location with an accuracy of 79% (p < .001). These findings are discussed in terms of their implications for visuospatial attention decoding and future paths for research are proposed. [ABSTRACT FROM AUTHOR]

Abstract: The time course of neural activity in human brain regions involved in mediating pursuit eye movements is unclear. To address this question, we recorded intracerebral electroencephalography activity in eight epileptic patients while they performed a pursuit task that dissociates reactive, predictive and inhibited pursuits. A sustained gamma band (50–150Hz) activity corresponding to pursuit maintenance was observed in the pursuit (and not saccade) area of the frontal eye field (FEF), in the ventral intraparietal sulcus (VIPS) and in occipital areas. The latency of gamma increase was found to precede target onset in FEF and VIPS, suggesting that those areas could also be involved during pursuit preparation/initiation. During pursuit inhibition, a sustained gamma band response was observed within prefrontal areas (pre-supplementary-motor-area, dorso-lateral prefrontal and frontopolar cortex). This study describes for the first time the dynamics of the neural activity in four areas of the pursuit system, not previously available in humans. These findings provide novel timing constraints to current models of the human pursuit system and establish the relevance of direct recordings to precisely relate eye movement behavior with neural activity in humans. [Copyright &y& Elsevier]

Reading sentences involves a distributed network of brain regions acting in concert surrounding the left sylvian fissure. The mechanisms of neural communication underlying the extraction and integration of verbal information across subcomponents of this reading network are still largely unknown. We recorded intracranial EEG activity in 12 epileptic human patients performing natural sentence reading and analyzed long-range corticocortical interactions between local neural activations. During a simple task contrasting semantic, phonological, and purely visual processes, we found process-specific neural activity elicited at the single-trial level, characterized by energy increases in a broad gamma band (40 -150 Hz). Correlation analysis between task-induced gamma-band activations revealed a selective fragmentation of the network into specialized subnetworks supporting sentence-level semantic analysis and phonological processing. We extend the implications of our results beyond reading, to propose that gamma-band amplitude correlations might constitute a fundamental mechanism for large-scale neural integration during high-level cognition. [ABSTRACT FROM AUTHOR]

Abstract: The orbitofrontal cortex (OFC) plays a key role not only in processing emotions but also in monitoring performance outcome. Although the neuroanatomical substrates underlying each of the two processes have been extensively investigated, they have predominantly been probed separately and therefore a precise knowledge of the functional overlap within the multiple OFC sub-portions involved is still lacking. Here, we explore the neural dynamics mediating performance monitoring and emotional processing using direct intracranial EEG (iEEG) recordings from multiple OFC sites of an epileptic patient. Neural activity was recorded during two experiments. The first task required processing of emotional faces and the second investigated action outcome evaluation based on a visual feedback on the subject''s performance. Task-related neural dynamics were assessed using modulations of high frequency responses in the gamma-band (50–150Hz). Our results reveal that processing negative facial emotions as well as receiving negative feedback both elicited gamma-band responses in the lateral OFC. By contrast, the mid-OFC was selectively activated for positive feedback. Furthermore, we also found significant gamma-band deactivation in the gyrus rectus during processing of negative feedback. Our findings provide novel evidence for an intricate valence-selective interaction between the networks mediating emotion processing and performance monitoring in human OFC and support the hypothesis of a tight relationship between gamma-band activity and behavior. [Copyright &y& Elsevier]

To celebrate the first 10 years of Nature Reviews Neuroscience, we invited the authors of the most cited article of each year to look back on the state of their field of research at the time of publication and the impact their article has had, and to discuss the questions that might be answered in the next 10 years. This selection of highly cited articles provides interesting snapshots of the progress that has been made in diverse areas of neuroscience. They show the enormous influence of neuroimaging techniques and highlight concepts that have generated substantial interest in the past decade, such as neuroimmunology, social neuroscience and the 'network approach' to brain function. These advancements will pave the way for further exciting discoveries that lie ahead. [ABSTRACT FROM AUTHOR]

We describe a method to detect brain activation in cortically constrained maps of current density computed from magnetoencephalography (MEG) data using multivariate statistical inference. We apply time-frequency (wavelet) analysis to individual epochs to produce dynamic images of brain signal power on the cerebral cortex in multiple time-frequency bands. We form vector observations by concatenating the power in each frequency band, and fit them into separate multivariate linear models for each time band and cortical location with experimental conditions as predictor variables. The resulting Roy's maximum root statistic maps are thresholded for significance using permutation tests and the maximum statistic approach. A source is considered significant if it exceeds a statistical threshold, which is chosen to control the familywise error rate, or the probability of at least one false positive, across the cortical surface. We compare and evaluate the multivariate approach with existing univariate approaches to time-frequency MEG signal analysis, both on simulated data and experimental data from an MEG visuomotor task study. Our results indicate that the multivariate method is more powerful than the univariate approach in detecting experimental effects when correlations exist between power across frequency bands. We further describe protected F-tests and linear discriminant analysis to identify individual frequencies that contribute significantly to experimental effects. Hum Brain Mapp, 2009. © 2009 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Verbal fluency (VF) is a heterogeneous cognitive function that requires executive as well as language abilities. The purpose of this study was to elucidate the specificity of the resting state MEG correlates of the executive and language components. To this end, we administered a VF test, another verbal test (Vocabulary), and another executive test (Trail Making Test), and we recorded 5-min eyes-open resting-state MEG data in 28 healthy participants. We used source-reconstructed spectral power estimates to compute correlation/anticorrelation MEG clusters with the performance at each test, as well as with the advantage in performance between tests, across individuals using cluster-level statistics in the standard frequency bands. By obtaining conjunction clusters between verbal fluency scores and factor loading obtained for verbal fluency and each of the two other tests, we showed a core of slow clusters (delta to beta) localized in the right hemisphere, in adjacent parts of the premotor, pre-central and post-central cortex in the mid-lateral regions related to executive monitoring. We also found slow parietal clusters bilaterally and a cluster in the gamma 2 and 3 bands in the left inferior frontal gyrus likely associated with phonological processing involved in verbal fluency. [ABSTRACT FROM AUTHOR]

How do we choose a particular action among equally valid alternatives? Nonhuman primate findings have shown that decision-making implicates modulations in unit firing rates and local field potentials (LFPs) across frontal and parietal cortices. Yet the electrophysiological brain mechanisms that underlie free choice in humans remain ill defined. Here, we address this question using rare intracerebral electroencephalography (EEG) recordings in surgical epilepsy patients performing a delayed oculomotor decision task. We find that the temporal dynamics of high-gamma (HG, 60–140 Hz) neural activity in distinct frontal and parietal brain areas robustly discriminate free choice from instructed saccade planning at the level of single trials. Classification analysis was applied to the LFP signals to isolate decision-related activity from sensory and motor planning processes. Compared with instructed saccades, free-choice trials exhibited delayed and longer-lasting HG activity during the delay period. The temporal dynamics of the decision-specific sustained HG activity indexed the unfolding of a deliberation process, rather than memory maintenance. Taken together, these findings provide the first direct electrophysiological evidence in humans for the role of sustained high-frequency neural activation in frontoparietal cortex in mediating the intrinsically driven process of freely choosing among competing behavioral alternatives. How do we choose a particular action among equally valid alternatives? Intracranial recordings in humans performing a delayed oculomotor decision task reveal that deliberating between competing alternatives during free choice is indexed by sustained high frequency neural activity in a network of parieto-frontal brain areas. [ABSTRACT FROM AUTHOR]

Visual object recognition seems to occur almost instantaneously. However, not only does it require hundreds of milliseconds of processing, but our eyes also typically fixate the object for hundreds of milliseconds. Consequently, information reaching our eyes at different moments is processed in the brain together. Moreover, information received at different moments during fixation is likely to be processed differently, notably because different features might be selectively attended at different moments. Here, we introduce a novel reverse correlation paradigm that allows us to uncover with millisecond precision the processing time course of specific information received on the retina at specific moments. Using faces as stimuli, we observed that processing at several electrodes and latencies was different depending on the moment at which information was received. Some of these variations were caused by a disruption occurring 160–200 ​ms after the face onset, suggesting a role of the N170 ERP component in gating information processing; others hinted at temporal compression and integration mechanisms. Importantly, the observed differences were not explained by simple adaptation or repetition priming, they were modulated by the task, and they were correlated with differences in behavior. These results suggest that top-down routines of information sampling are applied to the continuous visual input, even within a single eye fixation. • Light reaching our eyes at different moments is processed in the brain simultaneously. • We introduce a method to uncover the processing specific to each of these moments. • Processing is different depending on when information is received on the retina. • These variations are multiple and occur within a single short fixation. • These variations are at least partly top-down in origin and translate to behavior. [ABSTRACT FROM AUTHOR]

Despite being the focus of a thriving field of research, the biological mechanisms that underlie information integration in the brain are not yet fully understood. A theory that has gained a lot of traction in recent years suggests that multi-scale integration is regulated by a hierarchy of mutually interacting neural oscillations. In particular, there is accumulating evidence that phase-amplitude coupling (PAC), a specific form of cross-frequency interaction, plays a key role in numerous cognitive processes. Current research in the field is not only hampered by the absence of a gold standard for PAC analysis, but also by the computational costs of running exhaustive computations on large and high-dimensional electrophysiological brain signals. In addition, various signal properties and analyses parameters can lead to spurious PAC. Here, we present Tensorpac, an open-source Python toolbox dedicated to PAC analysis of neurophysiological data. The advantages of Tensorpac include (1) higher computational efficiency thanks to software design that combines tensor computations and parallel computing, (2) the implementation of all most widely used PAC methods in one package, (3) the statistical analysis of PAC measures, and (4) extended PAC visualization capabilities. Tensorpac is distributed under a BSD-3-Clause license and can be launched on any operating system (Linux, OSX and Windows). It can be installed directly via pip or downloaded from Github (https://github.com/EtienneCmb/tensorpac). By making Tensorpac available, we aim to enhance the reproducibility and quality of PAC research, and provide open tools that will accelerate future method development in neuroscience. [ABSTRACT FROM AUTHOR]

Background: Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Treatments against ALL might lead to later cognitive effects and alterations in brain structure in survivors but to the authors' knowledge the observed variability in the severity of neurocognitive deficits is not fully understood. The objective of the current study was to investigate abnormalities in visual short-term memory (VSTM) brain activation in survivors of childhood ALL using magnetoencephalography.Methods: A VSTM task was completed by 40 survivors of ALL and 26 controls. VSTM capacity (Cowan K) and brain activation were assessed during the retention period of the task (400-1400 milliseconds) using a standard minimum norm source localization method.Results: Performance (Cowan K) was found to be similar between survivors of ALL and controls. Atypical brain activation was found in survivors of ALL during the task, including overactivation of regions usually involved in VSTM (lateral occipital, precentral gyrus, and postcentral gyrus), recruitment of regions that typically are not involved in VSTM (superior/middle temporal gyrus and supramarginal gyrus), and lower activation of frontal brain regions (inferior frontal gyrus). These patterns of activation were modulated by the age at the time of cancer onset (P = .01) because activity was found to be reduced in participants who were younger at diagnosis.Conclusions: The results of the current study suggest a pattern of neural inefficiency and compensatory activity during VSTM in survivors of ALL. [ABSTRACT FROM AUTHOR]

During bimanual coordination, that is, manipulating with the dominant hand an object held by the postural hand, anticipatory postural adjustments are required to cancel the perturbations and ensure postural stabilization. Using magnetoencephalography (MEG), we investigated changes mediating the acquisition of anticipatory postural adjustments during a bimanual load‐lifting task. Participants lifted a load with their right hand, hence triggering the fall of a second load fixed to their left (postural) forearm. During Acquisition, the onset of load‐lifting and the fall of the second load were experimentally delayed after few trials. During Control, load‐lifting triggered the fall of the second load without delay. Upward elbow rotation decreased with trial repetition during Acquisition, hence attesting the ongoing acquisition of anticipatory postural adjustments. Bilateral event‐related desynchronisation (ERD) of the alpha rhythm (8–12 Hz) was recorded. Generators of the mu rhythm were found within central and associative motor regions. Their spatial distribution within the hemisphere contralateral to the load‐lifting arm was less refined and circumscribed during Acquisition compared to Control. Regression analyses emphasized the specific involvement of the precuneus in the right hemisphere contralateral to the postural forearm, and a medial prefrontal region in the left hemisphere. Analyses of the time course power showed that an increase in preunloading activation within the precuneus and a decrease in postunloading inhibition within the medial prefrontal region were associated with the acquisition of anticipatory postural adjustments. The study provides original insights into cortical activations mediating the progressive tuning of anticipatory postural adjustments during the acquisition stage of motor learning. [ABSTRACT FROM AUTHOR]

Rhythmic neuronal synchronization across large-scale networks is thought to play a key role in the regulation of conscious states. Changes in neuronal oscillation amplitude across states of consciousness have been widely reported, but little is known about possible changes in the temporal dynamics of these oscillations. The temporal structure of brain oscillations may provide novel insights into the neural mechanisms underlying consciousness. To address this question, we examined long-range temporal correlations (LRTC) of EEG oscillation amplitudes recorded during both wakefulness and anesthetic-induced unconsciousness. Importantly, the time-varying EEG oscillation envelopes were assessed over the course of a sevoflurane sedation protocol during which the participants alternated between states of consciousness and unconsciousness. Both spectral power and LRTC in oscillation amplitude were computed across multiple frequency bands. State-dependent differences in these features were assessed using non-parametric tests and supervised machine learning. We found that periods of unconsciousness were associated with increases in LRTC in beta (15–30Hz) amplitude over frontocentral channels and with a suppression of alpha (8–13Hz) amplitude over occipitoparietal electrodes. Moreover, classifiers trained to predict states of consciousness on single epochs demonstrated that the combination of beta LRTC with alpha amplitude provided the highest classification accuracy (above 80%). These results suggest that loss of consciousness is accompanied by an augmentation of temporal persistence in neuronal oscillation amplitude, which may reflect an increase in regularity and a decrease in network repertoire compared to the brain's activity during resting-state consciousness. [ABSTRACT FROM AUTHOR]

When combined with source modeling, magneto- (MEG) and electroencephalography (EEG) can be used to study long-range interactions among cortical processes non-invasively. Estimation of such inter-areal connectivity is nevertheless hindered by instantaneous field spread and volume conduction, which artificially introduce linear correlations and impair source separability in cortical current estimates. To overcome the inflating effects of linear source mixing inherent to standard interaction measures, alternative phase- and amplitude-correlation based connectivity measures, such as imaginary coherence and orthogonalized amplitude correlation have been proposed. Being by definition insensitive to zero-lag correlations, these techniques have become increasingly popular in the identification of correlations that cannot be attributed to field spread or volume conduction. We show here, however, that while these measures are immune to the direct effects of linear mixing, they may still reveal large numbers of spurious false positive connections through field spread in the vicinity of true interactions. This fundamental problem affects both region-of-interest-based analyses and all-to-all connectome mappings. Most importantly, beyond defining and illustrating the problem of spurious, or “ghost” interactions, we provide a rigorous quantification of this effect through extensive simulations. Additionally, we further show that signal mixing also significantly limits the separability of neuronal phase and amplitude correlations. We conclude that spurious correlations must be carefully considered in connectivity analyses in MEG/EEG source space even when using measures that are immune to zero-lag correlations. [ABSTRACT FROM AUTHOR]

• Class imbalance is common issue in the application of machine learning (ML) to neuroscience and can have severe consequences if not handled properly. • The impact of increasing data imbalance on ML performance is assessed for various levels of imbalance using simulated data, as well as EEG, MEG and fMRI recordings. • In highly imbalanced data, the commonly used Accuracy metric yields misleadingly high performances that result from systematically predicting the majority class. • The balanced accuracy (BAcc) metric is recommended as a default evaluation metric for ML, when seeking to minimize overall classification error. • A list of recommendations for dealing with imbalanced data is provided, and open-source code is made available to allow for further investigation. Machine learning (ML) is increasingly used in cognitive, computational and clinical neuroscience. The reliable and efficient application of ML requires a sound understanding of its subtleties and limitations. Training ML models on datasets with imbalanced classes is a particularly common problem, and it can have severe consequences if not adequately addressed. With the neuroscience ML user in mind, this paper provides a didactic assessment of the class imbalance problem and illustrates its impact through systematic manipulation of data imbalance ratios in (i) simulated data and (ii) brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). Our results illustrate how the widely-used Accuracy (Acc) metric, which measures the overall proportion of successful predictions, yields misleadingly high performances, as class imbalance increases. Because Acc weights the per-class ratios of correct predictions proportionally to class size, it largely disregards the performance on the minority class. A binary classification model that learns to systematically vote for the majority class will yield an artificially high decoding accuracy that directly reflects the imbalance between the two classes, rather than any genuine generalizable ability to discriminate between them. We show that other evaluation metrics such as the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC), and the less common Balanced Accuracy (BAcc) metric - defined as the arithmetic mean between sensitivity and specificity, provide more reliable performance evaluations for imbalanced data. Our findings also highlight the robustness of Random Forest (RF), and the benefits of using stratified cross-validation and hyperprameter optimization to tackle data imbalance. Critically, for neuroscience ML applications that seek to minimize overall classification error, we recommend the routine use of BAcc, which in the specific case of balanced data is equivalent to using standard Acc, and readily extends to multi-class settings. Importantly, we present a list of recommendations for dealing with imbalanced data, as well as open-source code to allow the neuroscience community to replicate and extend our observations and explore alternative approaches to coping with imbalanced data. [ABSTRACT FROM AUTHOR]

• Aperiodic EEG, but not oscillation, correlates with level of consciousness. • Anesthesia steepens the EEG spectral slope across disorders of consciousness. • Changes in EEG spectral slope reflect pre-anesthetic level of responsiveness. • Changes in the aperiodic EEG accompany loss of signal complexity and criticality. In the human electroencephalogram (EEG), oscillatory power co-exist with non-oscillatory, aperiodic activity. Although EEG analysis has traditionally focused exclusively on oscillatory power, recent investigations have shown that the aperiodic EEG component can distinguish conscious wakefulness from sleep and anesthetic-induced unconsciousness. This study investigates the aperiodic EEG component of individuals in a disorder of consciousness (DOC); how it changes in response to exposure to anesthesia; and how it relates to the brain's information richness and criticality. High-density EEG was recorded from 43 individuals in a DOC, with 16 of these individuals undergoing a protocol of propofol anesthesia. The aperiodic component was defined by the spectral slope of the power spectral density. Our results demonstrate that the EEG aperiodic component is more informative about the participants' level of consciousness than the oscillatory component, especially for patients that suffered from a stroke. Importantly, the pharmacologically induced change in the spectral slope from 30 to 45 Hz positively correlated with individual's pre-anesthetic level of consciousness. The pharmacologically induced loss of information-richness and criticality was associated with individual's pre-anesthetic aperiodic component. During exposure to anesthesia, the aperiodic component distinguished individuals with DOC, according to their 3-month recovery status. The aperiodic EEG component has been historically neglected; this research highlights the necessity of considering this measure for the assessment of individuals in DOC and future research that seeks to understand the neurophysiological underpinnings of consciousness. [Display omitted] [ABSTRACT FROM AUTHOR]

Objective Neuroimaging studies provide evidence of disturbed resting-state brain networks in Schizophrenia (SZ). However, untangling the neuronal mechanisms that subserve these baseline alterations requires measurement of their electrophysiological underpinnings. This systematic review specifically investigates the contributions of resting-state Magnetoencephalography (MEG) in elucidating abnormal neural organization in SZ patients. Method A systematic literature review of resting-state MEG studies in SZ was conducted. This literature is discussed in relation to findings from resting-state fMRI and EEG, as well as to task-based MEG research in SZ population. Importantly, methodological limitations are considered and recommendations to overcome current limitations are proposed. Results Resting-state MEG literature in SZ points towards altered local and long-range oscillatory network dynamics in various frequency bands. Critical methodological challenges with respect to experiment design, and data collection and analysis need to be taken into consideration. Conclusion Spontaneous MEG data show that local and global neural organization is altered in SZ patients. MEG is a highly promising tool to fill in knowledge gaps about the neurophysiology of SZ. However, to reach its fullest potential, basic methodological challenges need to be overcome. Significance MEG-based resting-state power and connectivity findings could be great assets to clinical and translational research in psychiatry, and SZ in particular. [ABSTRACT FROM AUTHOR]

Despite numerous important contributions, the investigation of brain connectivity with magnetoencephalography (MEG) still faces multiple challenges. One critical aspect of source-level connectivity, largely overlooked in the literature, is the putative effect of the choice of the inverse method on the subsequent cortico-cortical coupling analysis. We set out to investigate the impact of three inverse methods on source coherence detection using simulated MEG data. To this end, thousands of randomly located pairs of sources were created. Several parameters were manipulated, including inter- and intra-source correlation strength, source size and spatial configuration. The simulated pairs of sources were then used to generate sensor-level MEG measurements at varying signal-to-noise ratios (SNR). Next, the source level power and coherence maps were calculated using three methods (a) L2-Minimum-Norm Estimate (MNE), (b) Linearly Constrained Minimum Variance (LCMV) beamforming, and (c) Dynamic Imaging of Coherent Sources (DICS) beamforming. The performances of the methods were evaluated using Receiver Operating Characteristic (ROC) curves. The results indicate that beamformers perform better than MNE for coherence reconstructions if the interacting cortical sources consist of point-like sources. On the other hand, MNE provides better connectivity estimation than beamformers, if the interacting sources are simulated as extended cortical patches, where each patch consists of dipoles with identical time series (high intra-patch coherence). However, the performance of the beamformers for interacting patches improves substantially if each patch of active cortex is simulated with only partly coherent time series (partial intra-patch coherence). These results demonstrate that the choice of the inverse method impacts the results of MEG source-space coherence analysis, and that the optimal choice of the inverse solution depends on the spatial and synchronization profile of the interacting cortical sources. The insights revealed here can guide method selection and help improve data interpretation regarding MEG connectivity estimation. [ABSTRACT FROM AUTHOR]

Fragile X Syndrome (FXS) is a neurodevelopmental genetic disorder associated with cognitive and behavioural deficits. In particular, neuronal habituation processes have been shown to be altered in FXS patients. Yet, while such deficits have been primarily explored using auditory stimuli, less is known in the visual modality. Here, we investigated the putative alteration of repetition suppression using faces in FXS patients compared to controls that had the same age distribution. Electroencephalographic (EEG) signals were acquired while participants were presented with 18 different faces, each repeated ten times successively. The repetition suppression effect was probed by comparing the brain responses to the first and second presentation, based on task-evoked event-related potentials (ERP) as well as on task-induced oscillatory activity. We found different patterns of habituation for controls and patients both in ERP and oscillatory power. While the N170 was not affected by face repetition in controls, it was altered in FXS patients. Conversely, while a repetition suppression effect was observed in the theta band (4–8 Hz) over frontal and parieto-occipital areas in controls, it was not seen in FXS patients. These results provide the first evidence for diminished ERP and oscillatory habituation effects in response to face repetitions in FXS. These findings extend previous observations of impairments in learning mechanisms and may be linked to deficits in the maturation processes of synapses caused by the mutation. The present study contributes to bridging the gap between animal models of synaptic plasticity dysfunctions and human research in FXS. [ABSTRACT FROM AUTHOR]

Goal-directed motor behavior is associated with changes in patterns of rhythmic neuronal activity across widely distributed brain areas. In particular, movement initiation and execution are mediated by patterns of synchronization and desynchronization that occur concurrently across distinct frequency bands and across multiple motor cortical areas. To date, motor-related local oscillatory modulations have been predominantly examined by quantifying increases or suppressions in spectral power. However, beyond signal power, spectral properties such as phase and phase-amplitude coupling (PAC) have also been shown to carry information with regards to the oscillatory dynamics underlying motor processes. Yet, the distinct functional roles of phase, amplitude and PAC across the planning and execution of goal-directed motor behavior remain largely elusive. Here, we address this question with unprecedented resolution thanks to multi-site intracerebral EEG recordings in human subjects while they performed a delayed motor task. To compare the roles of phase, amplitude and PAC, we monitored intracranial brain signals from 748 sites across six medically intractable epilepsy patients at movement execution, and during the delay period where motor intention is present but execution is withheld. In particular, we used a machine-learning framework to identify the key contributions of various neuronal responses. We found a high degree of overlap between brain network patterns observed during planning and those present during execution. Prominent amplitude increases in the delta (2–4 Hz) and high gamma (60–200 Hz) bands were observed during both planning and execution. In contrast, motor alpha (8–13 Hz) and beta (13–30 Hz) power were suppressed during execution, but enhanced during the delay period. Interestingly, single-trial classification revealed that low-frequency phase information, rather than spectral power change, was the most discriminant feature in dissociating action from intention. Additionally, despite providing weaker decoding, PAC features led to statistically significant classification of motor states, particularly in anterior cingulate cortex and premotor brain areas. These results advance our understanding of the distinct and partly overlapping involvement of phase, amplitude and the coupling between them, in the neuronal mechanisms underlying motor intentions and executions. [ABSTRACT FROM AUTHOR]

Background Cross-frequency interactions between distinct brain areas have been observed in connection with a variety of cognitive tasks. With electro- and magnetoencephalography (EEG/MEG) data, typical connectivity measures between two brain regions analyze a single quantity from each region within a specific frequency band; given the wideband nature of EEG/MEG signals, many statistical tests may be required to identify true coupling. Furthermore, because of the poor spatial resolution of activity reconstructed from EEG/MEG, some interactions may actually be due to the linear mixing of brain sources. New method In the present work, a method for the detection of cross-frequency functional connectivity in MEG data using canonical correlation analysis (CCA) is described. We demonstrate that CCA identifies correlated signals and also the frequencies that cause the correlation. We also implement a procedure to deal with linear mixing based on symmetry properties of cross-covariance matrices. Results Our tests with both simulated and real MEG data demonstrate that CCA is able to detect interacting locations and the frequencies that cause them, while accurately discarding spurious coupling. Comparison with existing methods Recent techniques look at time delays in the activity between two locations to discard spurious interactions, while we propose a linear mixing model and demonstrate its relationship with symmetry aspects of cross-covariance matrices. Conclusions Our tests indicate the benefits of the CCA approach in connectivity studies, as it allows the simultaneous evaluation of several possible combinations of cross-frequency interactions in a single statistical test. [ABSTRACT FROM AUTHOR]

Minimum Norm Estimation (MNE) is an inverse solution method widely used to reconstruct the source time series that underlie magnetoencephalography (MEG) data. MNE addresses the ill-posed nature of MEG source estimation through regularization (e.g., Tikhonov regularization). Selecting the best regularization parameter is a critical step. Generally, once set, it is common practice to keep the same coefficient throughout a study. However, it is yet to be known whether the optimal lambda for spectral power analysis of MEG source data coincides with the optimal regularization for source-level oscillatory coupling analysis. We addressed this question via extensive Monte-Carlo simulations of MEG data, where we generated 21,600 configurations of pairs of coupled sources with varying sizes, signal-to-noise ratio (SNR), and coupling strengths. Then, we searched for the Tikhonov regularization coefficients (lambda) that maximize detection performance for (a) power and (b) coherence. For coherence, the optimal lambda was two orders of magnitude smaller than the best lambda for power. Moreover, we found that the spatial extent of the interacting sources and SNR, but not the extent of coupling, were the main parameters affecting the best choice for lambda. Our findings suggest using less regularization when measuring oscillatory coupling compared to power estimation. [ABSTRACT FROM AUTHOR]

Background Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep scoring. New method Here we propose a sleep staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. The performance of the method was evaluated using polysomnographic data from 15 subjects (electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) recordings). The decision tree, or dendrogram, was obtained using a hierarchical clustering technique and a wide range of time and frequency-domain features were extracted. Feature selection was carried out using forward sequential selection and classification was evaluated using k -fold cross-validation. Results The dendrogram-based SVM (DSVM) achieved mean specificity, sensitivity and overall accuracy of 0.92, 0.74 and 0.88 respectively, compared to expert visual scoring. Restricting DSVM classification to data where both experts’ scoring was consistent (76.73% of the data) led to a mean specificity, sensitivity and overall accuracy of 0.94, 0.82 and 0.92 respectively. Comparison with existing methods The DSVM framework outperforms classification with more standard multi-class “one-against-all” SVM and linear-discriminant analysis. Conclusion The promising results of the proposed methodology suggest that it may be a valuable alternative to existing automatic methods and that it could accelerate visual scoring by providing a robust starting hypnogram that can be further fine-tuned by expert inspection. [ABSTRACT FROM AUTHOR]

• Good scientific practice (GSP) describes recommended methods and procedures. • GSP minimizes errors and biases, and facilitates collaboration and reproducibility. • We outline current and developing MEEG GSP insights reflecting the state of the art. • This overview includes resources, tools, and thoughts to support GSP in MEEG research. • In all parts of the research cycle, we identify an increased tendency to collaborate. Good scientific practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization. For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated and adapted to new findings. However, GSP also needs to be regularly revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasized intangible GSP: a general awareness of personal, organizational, and societal realities and how they can influence MEEG research. This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, it covers GSP with respect to data acquisition, analysis, reporting, and sharing, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges. Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favor collective and cooperative work, for both scientific and for societal reasons. [ABSTRACT FROM AUTHOR]

Abstract: Eye movements are a constant and essential component of natural vision, yet, most of our knowledge about the human visual system comes from experiments that restrict them. This experimental constraint is mostly in place to control visual stimuli presentation and to avoid artifacts in non-invasive measures of brain activity, however, this limitation can be overcome with intracranial EEG (iEEG) recorded from epilepsy patients. Moreover, the high-frequency components of the iEEG signal (between about 50 and 150Hz) can provide a proxy of population-level spiking activity in any cortical area during free-viewing. We combined iEEG with high precision eye-tracking to study fine temporal dynamics and functional specificity in the fusiform face (FFA) and visual word form area (VWFA) while patients inspected natural pictures containing faces and text. We defined the first local measure of visual (electrophysiological) responsiveness adapted to free-viewing in humans: amplitude modulations in the high-frequency activity range (50–150Hz) following fixations (fixation-related high-frequency response). We showed that despite the large size of receptive fields in the ventral occipito-temporal cortex, neural activity during natural vision of realistic cluttered scenes is mostly dependent upon the category of the foveated stimulus — suggesting that category-specificity is preserved during free-viewing and that attention mechanisms might filter out the influence of objects surrounding the fovea. [Copyright &y& Elsevier]

Abstract: Stimulus repetition can produce neural response attenuation in stimulus-category selective networks within the occipito-temporal lobe. It is hypothesized that this neural suppression reflects the functional sharpening of local neuronal assemblies which boosts information processing efficiency. This neural suppression phenomenon has been mainly reported during conditions of conscious stimulus perception. The question remains whether frequent stimuli processed in the absence of conscious perception also induce repetition suppression in those specialized networks. Using rare intracranial EEG recordings in the ventral occipito-temporal cortex (VOTC) of human epileptic patients we investigated neural repetition suppression in conditions of conscious and unconscious visual processing of words. To this end, we used an orthogonal design manipulating respectively stimulus repetition (frequent vs. unique stimuli) and conscious perception (masked vs. unmasked stimuli). By measuring the temporal dynamics of high-frequency broadband gamma activity in VOTC and testing for main and interaction effects, we report that early processing of words in word-form selective networks exhibits a temporal cascade of modulations by stimulus repetition and masking: neuronal attenuation initially is observed in response to repeated words (irrespective of consciousness), that is followed by a second modulation contingent upon word reportability (irrespective of stimulus repetition). Later on (>300ms post-stimulus), a significant effect of conscious perception on the extent of repetition suppression was observed. The temporal dynamics of consciousness, the recognition memory processes and their interaction revealed in this study advance our understanding of their contributions to the neural mechanisms of word processing in VOTC. [Copyright &y& Elsevier]

Abstract: Epilepsy is, of course, not one disease but rather a huge number of disorders that can present with seizures. In common, they all reflect brain dysfunction. Moreover, they can affect the mind and, of course, behavior. While animals too may suffer from epilepsy, as far as we know, the electrical discharges are less likely to affect the mind and behavior, which is not surprising. While the epileptic seizures themselves are episodic, the mental and behavioral changes continue, in many cases, interictally. The episodic mental and behavioral manifestations are more dramatic, while the interictal ones are easier to study with anatomical and functional studies. The following extended summaries complement those presented in Part 1. [Copyright &y& Elsevier]

Abstract: The functional electrophysiology of the human cerebellum remains poorly characterized. Existing knowledge originates primarily from lesion studies and increasingly from hemodynamic measures such as functional magnetic resonance imaging, along with some evidence in recent years from transcranial magnetic stimulation. In this context, we revisit the few existing records of intracranial recordings from the human cerebellum, and uncover additional little-known reports – three from the Soviet Union, published in Russian between 1949 and 1951, and one from Belgium, published in French in 1964. These studies together demonstrate electrical rhythms of the human cerebellar cortex at frequencies as high as 250Hz, including task-related modulations. A reanalysis of their electrode traces with state-of-the-art spectral analysis techniques confirm the reported frequency bands, and showed that these modulations were sustained for 100–200ms. These remarkable observations from the early ages of intracranial mapping of the human brain are in line with recent electrophysiological studies of oscillations in the rodent cerebellum as well as magnetoencephalographic findings in humans. Time–frequency analyses have provided valuable insight into the function of cerebral cortex, and may prove even more critical for the differing neurophysiology of the cerebellum. We contend that these insights will be invaluable to bridge the role of oscillatory networks in the cerebellum with those of cerebral cortex in mediating perception, action, and cognition and to investigate possible cerebellar involvement in neurological dysfunction. [Copyright &y& Elsevier]

Abstract: Magnetoencephalographic (MEG) recordings are a rich source of information about the neural dynamics underlying cognitive processes in the brain, with excellent temporal and good spatial resolution. In recent years there have been considerable advances in MEG hardware developments and methods. Sophisticated analysis techniques are now routinely applied and continuously improved, leading to fascinating insights into the intricate dynamics of neural processes. However, the rapidly increasing level of complexity of the different steps in a MEG study make it difficult for novices, and sometimes even for experts, to stay aware of possible limitations and caveats. Furthermore, the complexity of MEG data acquisition and data analysis requires special attention when describing MEG studies in publications, in order to facilitate interpretation and reproduction of the results. This manuscript aims at making recommendations for a number of important data acquisition and data analysis steps and suggests details that should be specified in manuscripts reporting MEG studies. These recommendations will hopefully serve as guidelines that help to strengthen the position of the MEG research community within the field of neuroscience, and may foster discussion in order to further enhance the quality and impact of MEG research. [Copyright &y& Elsevier]

As you might experience it while reading this sentence, silent reading often involves an imagery speech component: we can hear our own "inner voice" pronouncing words mentally. Recent functional magnetic resonance imaging studies have associated that component with increased metabolic activity in the auditory cortex, including voice-selective areas. It remains to be determined, however, whether this activation arises automatically from early bottom-up visual inputs or whether it depends on late top-down control processes modulated by task demands. To answer this question, we collaborated with four epileptic human patients recorded with intracranial electrodes in the auditory cortex for therapeutic purposes, and measured high-frequency (50-150 Hz) "gamma" activity as a proxy of population level spiking activity. Temporal voice-selective areas (TVAs) were identified with an auditory localizer task and monitored as participants viewed words flashed on screen. We compared neural responses depending on whether words were attended or ignored and found a significant increase of neural activity in response to words, strongly enhanced by attention. In one of the patients, we could record that response at 800 ms in TVAs, but also at 700 ms in the primary auditory cortex and at 300 ms in the ventral occipital temporal cortex. Furthermore, single-trial analysis revealed a considerable jitter between activation peaks in visual and auditory cortices. Altogether, our results demonstrate that the multimodal mental experience of reading is in fact a heterogeneous complex of asynchronous neural responses, and that auditory and visual modalities often process distinct temporal frames of our environment at the same time. [ABSTRACT FROM AUTHOR]