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Previous studies have found electroencephalogram (EEG) amplitude and scalp topography differences between neurotypical and neurological/neurosurgical groups, being interpreted at the cognitive level. However, these comparisons are invariably accompanied by anatomical changes. Critical to EEG are the so-called volume currents, which are affected by the spatial distribution of the different tissues in the head. We investigated the effect of cerebrospinal fluid (CSF)-filled cavities on simulated EEG scalp data. We simulated EEG scalp potentials for known sources using different volume conduction models: a reference model (i.e., unlesioned brain) and models with realistic CSF-filled cavities gradually increasing in size. We used this approach for a single source close or far from the CSF-lesion cavity, and for a scenario with a distributed configuration of sources (i.e., a "cognitive event-related potential effect"). The magnitude and topography errors between the reference and lesion models were quantified. For the single-source simulation close to the lesion, the CSF-filled lesion modulated signal amplitude with more than 17% magnitude error and topography with more than 9% topographical error. Negligible modulation was found for the single source far from the lesion. For the multisource simulations of the cognitive effect, the CSF-filled lesion modulated signal amplitude with more than 6% magnitude error and topography with more than 16% topography error in a nonmonotonic fashion. In conclusion, the impact of a CSF-filled cavity cannot be neglected for scalp-level EEG data. Especially when group-level comparisons are made, any scalp-level attenuated, aberrant, or absent effects are difficult to interpret without considering the confounding effect of CSF., (© 2024 The Author(s). Psychophysiology published by Wiley Periodicals LLC on behalf of Society for Psychophysiological Research.)

An intriguing question in cognitive neuroscience is whether alpha oscillations shape how the brain transforms the continuous sensory inputs into distinct percepts. According to the alpha temporal resolution hypothesis, sensory signals arriving within a single alpha cycle are integrated, whereas those in separate cycles are segregated. Consequently, shorter alpha cycles should be associated with smaller temporal binding windows and higher temporal resolution. However, the evidence supporting this hypothesis is contentious, and the neural mechanisms remain unclear. In this review, we first elucidate the alpha temporal resolution hypothesis and the neural circuitries that generate alpha oscillations. We then critically evaluate study designs, experimental paradigms, psychophysics, and neurophysiological analyses that have been employed to investigate the role of alpha frequency in temporal binding. Through the lens of this methodological framework, we then review evidence from between-subject, within-subject, and causal perturbation studies. Our review highlights the inherent interpretational ambiguities posed by previous study designs and experimental paradigms and the extensive variability in analysis choices across studies. We also suggest best practice recommendations that may help to guide future research. To establish a mechanistic role of alpha frequency in temporal parsing, future research is needed that demonstrates its causal effects on the temporal binding window with consistent, experimenter-independent methods., (© 2024 Massachusetts Institute of Technology.)

The papers collected in this Special Focus, prompted by S. Buergers and U. Noppeney [The role of alpha oscillations in temporal binding within and across the senses. Nature Human Behaviour, 6, 732-742, 2022], have raised several interesting ideas, arguments, and empirical results relating to the alpha temporal resolution hypothesis. Here we briefly respond to these, and in the process emphasize four challenges for future research: defining the scope and limitation of the hypothesis; developing experimental paradigms and study designs that rigorously test its tenets; decomposing the scalp-level signal and isolating underlying neural circuits; and bringing uniformity to the current diversity of analysis and statistical methods. Addressing these challenges will facilitate the progression from merely correlating alpha frequency with various perceptual phenomena to establishing whether and (if so) how alpha frequency influences sensory integration and segregation., (© 2024 Massachusetts Institute of Technology.)

During communication in real-life settings, our brain often needs to integrate auditory and visual information and at the same time actively focus on the relevant sources of information, while ignoring interference from irrelevant events. The interaction between integration and attention processes remains poorly understood. Here, we use rapid invisible frequency tagging and magnetoencephalography to investigate how attention affects auditory and visual information processing and integration, during multimodal communication. We presented human participants (male and female) with videos of an actress uttering action verbs (auditory; tagged at 58 Hz) accompanied by two movie clips of hand gestures on both sides of fixation (attended stimulus tagged at 65 Hz; unattended stimulus tagged at 63 Hz). Integration difficulty was manipulated by a lower-order auditory factor (clear/degraded speech) and a higher-order visual semantic factor (matching/mismatching gesture). We observed an enhanced neural response to the attended visual information during degraded speech compared to clear speech. For the unattended information, the neural response to mismatching gestures was enhanced compared to matching gestures. Furthermore, signal power at the intermodulation frequencies of the frequency tags, indexing nonlinear signal interactions, was enhanced in the left frontotemporal and frontal regions. Focusing on the left inferior frontal gyrus, this enhancement was specific for the attended information, for those trials that benefitted from integration with a matching gesture. Together, our results suggest that attention modulates audiovisual processing and interaction, depending on the congruence and quality of the sensory input., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 the authors.)

When perceiving the world around us, we are constantly integrating pieces of information. The integrated experience consists of more than just the sum of its parts. For example, visual scenes are defined by a collection of objects as well as the spatial relations amongst them and sentence meaning is computed based on individual word semantic but also syntactic configuration. Having quantitative models of such integrated representations can help evaluate cognitive models of both language and scene perception. Here, we focus on language, and use a behavioral measure of perceived similarity as an approximation of integrated meaning representations. We collected similarity judgments of 200 subjects rating nouns or transitive sentences through an online multiple arrangement task. We find that perceived similarity between sentences is most strongly modulated by the semantic action category of the main verb. In addition, we show how non-negative matrix factorization of similarity judgment data can reveal multiple underlying dimensions reflecting both semantic as well as relational role information. Finally, we provide an example of how similarity judgments on sentence stimuli can serve as a point of comparison for artificial neural networks models (ANNs) by comparing our behavioral data against sentence similarity extracted from three state-of-the-art ANNs. Overall, our method combining the multiple arrangement task on sentence stimuli with matrix factorization can capture relational information emerging from integration of multiple words in a sentence even in the presence of strong focus on the verb., (© 2023. The Author(s).)

There remains some debate about whether beta power effects observed during sentence comprehension reflect ongoing syntactic unification operations (beta-syntax hypothesis), or instead reflect maintenance or updating of the sentence-level representation (beta-maintenance hypothesis). In this study, we used magnetoencephalography to investigate beta power neural dynamics while participants read relative clause sentences that were initially ambiguous between a subject- or an object-relative reading. An additional condition included a grammatical violation at the disambiguation point in the relative clause sentences. The beta-maintenance hypothesis predicts a decrease in beta power at the disambiguation point for unexpected (and less preferred) object-relative clause sentences and grammatical violations, as both signal a need to update the sentence-level representation. While the beta-syntax hypothesis also predicts a beta power decrease for grammatical violations due to a disruption of syntactic unification operations, it instead predicts an increase in beta power for the object-relative clause condition because syntactic unification at the point of disambiguation becomes more demanding. We observed decreased beta power for both the agreement violation and object-relative clause conditions in typical left hemisphere language regions, which provides compelling support for the beta-maintenance hypothesis. Mid-frontal theta power effects were also present for grammatical violations and object-relative clause sentences, suggesting that violations and unexpected sentence interpretations are registered as conflicts by the brain's domain-general error detection system., (© 2023 The Authors. Psychophysiology published by Wiley Periodicals LLC on behalf of Society for Psychophysiological Research.)

Speaking requires the temporally coordinated planning of core linguistic information, from conceptual meaning to articulation. Recent neurophysiological results suggested that these operations involve a cascade of neural events with subsequent onset times, whilst competing evidence suggests early parallel neural activation. To test these hypotheses, we examined the sources of neuromagnetic activity recorded from 34 participants overtly naming 134 images from 4 object categories (animals, tools, foods and clothes). Within each category, word length and phonological neighbourhood density were co-varied to target phonological/phonetic processes. Multivariate pattern analyses (MVPA) searchlights in source space decoded object categories in occipitotemporal and middle temporal cortex, and phonological/phonetic variables in left inferior frontal (BA 44) and motor cortex early on. The findings suggest early activation of multiple variables due to intercorrelated properties and interactivity of processing, thus raising important questions about the representational properties of target words during the preparatory time enabling overt speaking.

To understand language, we need to recognize words and combine them into phrases and sentences. During this process, responses to the words themselves are changed. In a step toward understanding how the brain builds sentence structure, the present study concerns the neural readout of this adaptation. We ask whether low-frequency neural readouts associated with words change as a function of being in a sentence. To this end, we analyzed an MEG dataset by Schoffelen et al. (2019) of 102 human participants (51 women) listening to sentences and word lists, the latter lacking any syntactic structure and combinatorial meaning. Using temporal response functions and a cumulative model-fitting approach, we disentangled delta- and theta-band responses to lexical information (word frequency), from responses to sensory and distributional variables. The results suggest that delta-band responses to words are affected by sentence context in time and space, over and above entropy and surprisal. In both conditions, the word frequency response spanned left temporal and posterior frontal areas; however, the response appeared later in word lists than in sentences. In addition, sentence context determined whether inferior frontal areas were responsive to lexical information. In the theta band, the amplitude was larger in the word list condition ∼100 milliseconds in right frontal areas. We conclude that low-frequency responses to words are changed by sentential context. The results of this study show how the neural representation of words is affected by structural context and as such provide insight into how the brain instantiates compositionality in language. SIGNIFICANCE STATEMENT Human language is unprecedented in its combinatorial capacity: we are capable of producing and understanding sentences we have never heard before. Although the mechanisms underlying this capacity have been described in formal linguistics and cognitive science, how they are implemented in the brain remains to a large extent unknown. A large body of earlier work from the cognitive neuroscientific literature implies a role for delta-band neural activity in the representation of linguistic structure and meaning. In this work, we combine these insights and techniques with findings from psycholinguistics to show that meaning is more than the sum of its parts; the delta-band MEG signal differentially reflects lexical information inside and outside sentence structures., (Copyright © 2023 the authors.)

Source reconstruction of magnetoencephalography (MEG) has been used to assess brain reorganization after brain damage, such as stroke. Lesions result in parts of the brain having an electrical conductivity that differs from the normal values. The effect this has on the forward solutions (i.e., the propagation of electric currents and magnetic fields generated by cortical activity) is well predictable. However, their influence on source localization results is not well characterized and understood. This is specifically a concern for patient studies with asymmetric (i.e., within one hemisphere) lesions focusing on asymmetric and lateralized brain activity, such as language. In particular, it is good practice to consider the level of geometrical detail that is necessary to compute and interpret reliable source reconstruction results. To understand the effect of lesions on source estimates and propose recommendations to researchers working with clinical data, in this study we consider the trade off between improved accuracy and the additional effort to compute more realistic head models, with the aim to answer the question whether the additional effort is worth it. We simulated and analyzed the effects of a stroke lesion (i.e., an asymmetrically distributed CSF-filled cavity) in the head model with three different sizes and locations when performing MEG source reconstruction using a finite element method (FEM). We compared the effect of the lesion with a homogeneous head model that neglects the lesion. We computed displacement and attenuation/amplification maps to quantify the localization errors and signal magnitude modulation. We conclude that brain lesions leading to asymmetrically distributed CSF-filled cavities should be modeled when performing MEG source reconstruction, especially when investigating deep sources or post-stroke hemispheric lateralization of functions. The strongest effects are not only visible in perilesional areas, but can extend up to 20 mm from the lesion. Bigger lesions lead to stronger effects impacting larger areas, independently from the lesion location. Lastly, we conclude that more priority should be given to usability and accessibility of the required computational tools, to allow researchers with less technical expertise to use the improved methods that are available but currently not widely adopted yet., (Copyright © 2022. Published by Elsevier Inc.)

This study investigated two questions. One is: To what degree is sentence processing beyond single words independent of the input modality (speech vs. reading)? The second question is: Which parts of the network recruited by both modalities is sensitive to syntactic complexity? These questions were investigated by having more than 200 participants read or listen to well-formed sentences or series of unconnected words. A largely left-hemisphere frontotemporoparietal network was found to be supramodal in nature, i.e., independent of input modality. In addition, the left inferior frontal gyrus (LIFG) and the left posterior middle temporal gyrus (LpMTG) were most clearly associated with left-branching complexity. The left anterior temporal lobe showed the greatest sensitivity to sentences that differed in right-branching complexity. Moreover, activity in LIFG and LpMTG increased from sentence onset to end, in parallel with an increase of the left-branching complexity. While LIFG, bilateral anterior temporal lobe, posterior MTG, and left inferior parietal lobe all contribute to the supramodal unification processes, the results suggest that these regions differ in their respective contributions to syntactic complexity related processing. The consequences of these findings for neurobiological models of language processing are discussed., Competing Interests: Competing Interests: The authors have declared that no competing interests exist., (© 2022 Massachusetts Institute of Technology.)

Understanding spoken language requires transforming ambiguous acoustic streams into a hierarchy of representations, from phonemes to meaning. It has been suggested that the brain uses prediction to guide the interpretation of incoming input. However, the role of prediction in language processing remains disputed, with disagreement about both the ubiquity and representational nature of predictions. Here, we address both issues by analyzing brain recordings of participants listening to audiobooks, and using a deep neural network (GPT-2) to precisely quantify contextual predictions. First, we establish that brain responses to words are modulated by ubiquitous predictions. Next, we disentangle model-based predictions into distinct dimensions, revealing dissociable neural signatures of predictions about syntactic category (parts of speech), phonemes, and semantics. Finally, we show that high-level (word) predictions inform low-level (phoneme) predictions, supporting hierarchical predictive processing. Together, these results underscore the ubiquity of prediction in language processing, showing that the brain spontaneously predicts upcoming language at multiple levels of abstraction.

Language production involves a complex set of computations, from conceptualization to articulation, which are thought to engage cascading neural events in the language network. However, recent neuromagnetic evidence suggests simultaneous meaning-to-speech mapping in picture naming tasks, as indexed by early parallel activation of frontotemporal regions to lexical semantic, phonological, and articulatory information. Here we investigate the time course of word production, asking to what extent such "earliness" is a distinctive property of the associated spatiotemporal dynamics. Using MEG, we recorded the neural signals of 34 human subjects (26 males) overtly naming 134 images from four semantic object categories (animals, foods, tools, clothes). Within each category, we covaried word length, as quantified by the number of syllables contained in a word, and phonological neighborhood density to target lexical and post-lexical phonological/phonetic processes. Multivariate pattern analyses searchlights in sensor space distinguished the stimulus-locked spatiotemporal responses to object categories early on, from 150 to 250 ms after picture onset, whereas word length was decoded in left frontotemporal sensors at 250-350 ms, followed by the latency of phonological neighborhood density (350-450 ms). Our results suggest a progression of neural activity from posterior to anterior language regions for the semantic and phonological/phonetic computations preparing overt speech, thus supporting serial cascading models of word production. SIGNIFICANCE STATEMENT Current psycholinguistic models make divergent predictions on how a preverbal message is mapped onto articulatory output during the language planning. Serial models predict a cascading sequence of hierarchically organized neural computations from conceptualization to articulation. In contrast, parallel models posit early simultaneous activation of multiple conceptual, phonological, and articulatory information in the language system. Here we asked whether such earliness is a distinctive property of the neural dynamics of word production. The combination of the millisecond precision of MEG with multivariate pattern analyses revealed subsequent onset times for the neural events supporting semantic and phonological/phonetic operations, progressing from posterior occipitotemporal to frontal sensor areas. The findings bring new insights for refining current theories of language production., (Copyright © 2022 Carota et al.)

Recently, cognitive neuroscientists have increasingly studied the brain responses to narratives. At the same time, we are witnessing exciting developments in natural language processing where large-scale neural network models can be used to instantiate cognitive hypotheses in narrative processing. Yet, they learn from text alone and we lack ways of incorporating biological constraints during training. To mitigate this gap, we provide a narrative comprehension magnetoencephalography (MEG) data resource that can be used to train neural network models directly on brain data. We recorded from 3 participants, 10 separate recording hour-long sessions each, while they listened to audiobooks in English. After story listening, participants answered short questions about their experience. To minimize head movement, the participants wore MEG-compatible head casts, which immobilized their head position during recording. We report a basic evoked-response analysis showing that the responses accurately localize to primary auditory areas. The responses are robust and conserved across 10 sessions for every participant. We also provide usage notes and briefly outline possible future uses of the resource., (© 2022. The Author(s).)

Sustained attention has long been thought to benefit perception in a continuous fashion, but recent evidence suggests that it affects perception in a discrete, rhythmic way. Periodic fluctuations in behavioral performance over time, and modulations of behavioral performance by the phase of spontaneous oscillatory brain activity point to an attentional sampling rate in the theta or alpha frequency range. We investigated whether such discrete sampling by attention is reflected in periodic fluctuations in the decodability of visual stimulus orientation from magnetoencephalographic (MEG) brain signals. In this exploratory study, human subjects attended one of the two grating stimuli, while MEG was being recorded. We assessed the strength of the visual representation of the attended stimulus using a support vector machine (SVM) to decode the orientation of the grating (clockwise vs. counterclockwise) from the MEG signal. We tested whether decoder performance depended on the theta/alpha phase of local brain activity. While the phase of ongoing activity in the visual cortex did not modulate decoding performance, theta/alpha phase of activity in the frontal eye fields and parietal cortex, contralateral to the attended stimulus did modulate decoding performance. These findings suggest that phasic modulations of visual stimulus representations in the brain are caused by frequency-specific top-down activity in the frontoparietal attention network, though the behavioral relevance of these effects could not be established., (© 2020 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

Music is often described in the laboratory and in the classroom as a beneficial tool for memory encoding and retention, with a particularly strong effect when words are sung to familiar compared to unfamiliar melodies. However, the neural mechanisms underlying this memory benefit, especially for benefits related to familiar music are not well understood. The current study examined whether neural tracking of the slow syllable rhythms of speech and song is modulated by melody familiarity. Participants became familiar with twelve novel melodies over four days prior to MEG testing. Neural tracking of the same utterances spoken and sung revealed greater cerebro-acoustic phase coherence for sung compared to spoken utterances, but did not show an effect of familiar melody when stimuli were grouped by their assigned (trained) familiarity. However, when participant's subjective ratings of perceived familiarity were used to group stimuli, a large effect of familiarity was observed. This effect was not specific to song, as it was observed in both sung and spoken utterances. Exploratory analyses revealed some in-session learning of unfamiliar and spoken utterances, with increased neural tracking for untrained stimuli by the end of the MEG testing session. Our results indicate that top-down factors like familiarity are strong modulators of neural tracking for music and language. Participants' neural tracking was related to their perception of familiarity, which was likely driven by a combination of effects from repeated listening, stimulus-specific melodic simplicity, and individual differences. Beyond simply the acoustic features of music, top-down factors built into the music listening experience, like repetition and familiarity, play a large role in the way we attend to and encode information presented in a musical context., (Copyright © 2022. Published by Elsevier Inc.)

Typical adults read remarkably quickly. Such fast reading is facilitated by brain processes that are sensitive to both word frequency and contextual constraints. It is debated as to whether these attributes have additive or interactive effects on language processing in the brain. We investigated this issue by analysing existing magnetoencephalography data from 99 participants reading intact and scrambled sentences . Using a cross-validated model comparison scheme, we found that lexical frequency predicted the word-by-word elicited MEG signal in a widespread cortical network, irrespective of sentential context. In contrast, index (ordinal word position) was more strongly encoded in sentence words, in left front-temporal areas. This confirms that frequency influences word processing independently of predictability, and that contextual constraints affect word-by-word brain responses. With a conservative multiple comparisons correction, only the interaction between lexical frequency and surprisal survived, in anterior temporal and frontal cortex, and not between lexical frequency and entropy, nor between lexical frequency and index. However, interestingly, the uncorrected index × frequency interaction revealed an effect in left frontal and temporal cortex that reversed in time and space for intact compared to scrambled sentences. Finally, we provide evidence to suggest that, in sentences, lexical frequency and predictability may independently influence early (<150 ms) and late stages of word processing, but also interact during late stages of word processing (>150-250 ms), thus helping to converge previous contradictory eye-tracking and electrophysiological literature. Current neurocognitive models of reading would benefit from accounting for these differing effects of lexical frequency and predictability on different stages of word processing., Competing Interests: Competing Interests: The authors have declared that no competing interests exist., (© 2021 Massachusetts Institute of Technology.)

Beamforming is a popular method for functional source reconstruction using magnetoencephalography (MEG) and electroencephalography (EEG) data. Beamformers, which were first proposed for MEG more than two decades ago, have since been applied in hundreds of studies, demonstrating that they are a versatile and robust tool for neuroscience. However, certain characteristics of beamformers remain somewhat elusive and there currently does not exist a unified documentation of the mathematical underpinnings and computational subtleties of beamformers as implemented in the most widely used academic open source software packages for MEG analysis (Brainstorm, FieldTrip, MNE, and SPM). Here, we provide such documentation that aims at providing the mathematical background of beamforming and unifying the terminology. Beamformer implementations are compared across toolboxes and pitfalls of beamforming analyses are discussed. Specifically, we provide details on handling rank deficient covariance matrices, prewhitening, the rank reduction of forward fields, and on the combination of heterogeneous sensor types, such as magnetometers and gradiometers. The overall aim of this paper is to contribute to contemporary efforts towards higher levels of computational transparency in functional neuroimaging., (Copyright © 2021. Published by Elsevier Inc.)

Analyses of cerebro-peripheral connectivity aim to quantify ongoing coupling between brain activity (measured by MEG/EEG) and peripheral signals such as muscle activity, continuous speech, or physiological rhythms (such as pupil dilation or respiration). Due to the distinct rhythmicity of these signals, undirected connectivity is typically assessed in the frequency domain. This leaves the investigator with two critical choices, namely a) the appropriate measure for spectral estimation (i.e., the transformation into the frequency domain) and b) the actual connectivity measure. As there is no consensus regarding best practice, a wide variety of methods has been applied. Here we systematically compare combinations of six standard spectral estimation methods (comprising fast Fourier and continuous wavelet transformation, bandpass filtering, and short-time Fourier transformation) and six connectivity measures (phase-locking value, Gaussian-Copula mutual information, Rayleigh test, weighted pairwise phase consistency, magnitude squared coherence, and entropy). We provide performance measures of each combination for simulated data (with precise control over true connectivity), a single-subject set of real MEG data, and a full group analysis of real MEG data. Our results show that, overall, WPPC and GCMI tend to outperform other connectivity measures, while entropy was the only measure sensitive to bimodal deviations from a uniform phase distribution. For group analysis, choosing the appropriate spectral estimation method appears to be more critical than the connectivity measure. We discuss practical implications (sampling rate, SNR, computation time, and data length) and aim to provide recommendations tailored to particular research questions., Competing Interests: Declaration of Competing Interest None., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the "WU-Minn-Ox" HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The "HCP-style" neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflicts of interest in relation to this manuscript., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Alpha activity (8-14 Hz) is the dominant rhythm in the awake brain and is thought to play an important role in setting the internal state of the brain. Previous work has associated states of decreased alpha power with enhanced neural excitability. However, evidence is mixed on whether and how such excitability enhancement modulates sensory signals of interest versus noise differently, and what, if any, are the consequences for subsequent perception. Here, human subjects (male and female) performed a visual detection task in which we manipulated their decision criteria in a blockwise manner. Although our manipulation led to substantial criterion shifts, these shifts were not reflected in prestimulus alpha band changes. Rather, lower prestimulus alpha power in occipital-parietal areas improved perceptual sensitivity and enhanced information content decodable from neural activity patterns. Additionally, oscillatory alpha phase immediately before stimulus presentation modulated accuracy. Together, our results suggest that alpha band dynamics modulate sensory signals of interest more strongly than noise. SIGNIFICANCE STATEMENT The internal state of our brain fluctuates, giving rise to variability in perception and action. Neural oscillations, most prominently in the alpha band, have been suggested to play a role in setting this internal state. Here, we show that ongoing alpha band activity in occipital-parietal regions predicts the quality of visual information decodable in neural activity patterns and subsequently the human observer's sensitivity in a visual detection task. Our results provide comprehensive evidence that visual representation is modulated by ongoing alpha band activity and advance our understanding on how, when faced with unchanging external stimuli, internal neural fluctuations influence perception and behavior., (Copyright © 2021 the authors.)

The possibility to combine smaller units of meaning (e.g., words) to create new and more complex meanings (e.g., phrases and sentences) is a fundamental feature of human language. In the present project, we investigated how the brain supports the semantic and syntactic composition of two-word adjective-noun phrases in Dutch, using magnetoencephalography (MEG). The present investigation followed up on previous studies reporting a composition effect in the left anterior temporal lobe (LATL) when comparing neural activity at nouns combined with adjectives, as opposed to nouns in a non-compositional context. The first aim of the present study was to investigate whether this effect, as well as its modulation by noun specificity and adjective class, can also be observed in Dutch. A second aim was to investigate to what extent these effects may be driven by syntactic composition rather than primarily by semantic composition as was previously proposed. To this end, a novel condition was administered in which participants saw nouns combined with pseudowords lacking meaning but agreeing with the nouns in terms of grammatical gender, as real adjectives would. We failed to observe a composition effect or its modulation in both a confirmatory analysis (focused on the cortical region and time-window where it has previously been reported) and in exploratory analyses (where we tested multiple regions and an extended potential time-window of the effect). A syntactically driven composition effect was also not observed in our data. We do, however, successfully observe an independent, previously reported effect on single word processing in our data, confirming that our MEG data processing pipeline does meaningfully capture language processing activity by the brain. The failure to observe the composition effect in LATL is surprising given that it has been previously reported in multiple studies. Reviewing all previous studies investigating this effect, we propose that materials and a task involving imagery might be necessary for this effect to be observed. In addition, we identified substantial variability in the regions of interest analyzed in previous studies, which warrants additional checks of robustness of the effect. Further research should identify limits and conditions under which this effect can be observed. The failure to observe specifically a syntactic composition effect in such minimal phrases is less surprising given that it has not been previously reported in MEG data., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Rhythmic brain activity may reflect a functional mechanism that facilitates cortical processing and dynamic interareal interactions and thereby give rise to complex behavior. Using magnetoencephalography (MEG), we investigated rhythmic brain activity in a brain-wide network and their relation to behavior, while human subjects executed a variant of the Simon task, a simple stimulus-response task with well-studied behavioral effects. We hypothesized that the faster reaction times (RT) on stimulus-response congruent versus incongruent trials are associated with oscillatory power changes, reflecting a change in local cortical activation. Additionally, we hypothesized that the faster reaction times for trials following instances with the same stimulus-response contingency (the so-called Gratton effect) is related to contingency-induced changes in the state of the network, as measured by differences in local spectral power and interareal phase coherence. This would be achieved by temporarily upregulating the connectivity strength between behaviorally relevant network nodes. We identified regions-of-interest that differed in local synchrony during the response phase of the Simon task. Within this network, spectral power in none of the nodes in either of the studied frequencies was significantly different in the pre-cue window of the subsequent trial. Nor was there a significant difference in coherence between the task-relevant nodes that could explain the superior behavioral performance after compatible consecutive trials., (Copyright © 2020. Published by Elsevier Inc.)

The Organization for Human Brain Mapping (OHBM) has been active in advocating for the instantiation of best practices in neuroimaging data acquisition, analysis, reporting and sharing of both data and analysis code to deal with issues in science related to reproducibility and replicability. Here we summarize recommendations for such practices in magnetoencephalographic (MEG) and electroencephalographic (EEG) research, recently developed by the OHBM neuroimaging community known by the abbreviated name of COBIDAS MEEG. We discuss the rationale for the guidelines and their general content, which encompass many topics under active discussion in the field. We highlight future opportunities and challenges to maximizing the sharing and exploitation of MEG and EEG data, and we also discuss how this 'living' set of guidelines will evolve to continually address new developments in neurophysiological assessment methods and multimodal integration of neurophysiological data with other data types.

The human cortex is characterized by local morphological features such as cortical thickness, myelin content, and gene expression that change along the posterior-anterior axis. We investigated if some of these structural gradients are associated with a similar gradient in a prominent feature of brain activity - namely the frequency of oscillations. In resting-state MEG recordings from healthy participants (N = 187) using mixed effect models, we found that the dominant peak frequency in a brain area decreases significantly along the posterior-anterior axis following the global hierarchy from early sensory to higher order areas. This spatial gradient of peak frequency was significantly anticorrelated with that of cortical thickness, representing a proxy of the cortical hierarchical level. This result indicates that the dominant frequency changes systematically and globally along the spatial and hierarchical gradients and establishes a new structure-function relationship pertaining to brain oscillations as a core organization that may underlie hierarchical specialization in the brain., Competing Interests: KM, JS, AK, JG No competing interests declared, (© 2020, Mahjoory et al.)

Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (MNE-Python, FieldTrip, DAiSS (SPM12), and Brainstorm) using datasets both with and without SSS interference suppression. We analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with the signal-to-noise ratio (SNR) in all four toolboxes. When applied carefully to MEG data with a typical SNR (3-15 ​dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs for the first three toolboxes while Brainstorm showed greater robustness but with lower spatial resolution. We also found that the SNR improvement offered by SSS led to more accurate localization., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Different inputs from a multisensory object or event are often integrated into a coherent and unitary percept, despite differences in sensory formats, neural pathways, and processing times of the involved modalities. Presumably, multisensory integration occurs if the cross-modal inputs are presented within a certain window of temporal integration where inputs are perceived as being simultaneous. Here, we examine the role of ongoing neuronal alpha (i.e. 10-Hz) oscillations in multimodal synchrony perception. While EEG was measured, participants performed a simultaneity judgement task with visual stimuli preceding auditory ones. At stimulus onset asynchronies (SOA's) of 160-200 ms, simultaneity judgements were around 50%. For trials with these SOA's, occipital alpha power was smaller preceding correct judgements, and the individual alpha frequency was correlated with the size of the temporal window of integration. In addition, simultaneity judgements were modulated as a function of oscillatory phase at 12.5 Hz, but the latter effect was only marginally significant. These results support the notion that oscillatory neuronal activity in the alpha frequency range, which has been taken to shape perceptual cycles, is instrumental in multisensory perception., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The meaning of a sentence can be understood, whether presented in written or spoken form. Therefore, it is highly probable that brain processes supporting language comprehension are at least partly independent of sensory modality. To identify where and when in the brain language processing is independent of sensory modality, we directly compared neuromagnetic brain signals of 200 human subjects (102 males) either reading or listening to sentences. We used multiset canonical correlation analysis to align individual subject data in a way that boosts those aspects of the signal that are common to all, allowing us to capture word-by-word signal variations, consistent across subjects and at a fine temporal scale. Quantifying this consistency in activation across both reading and listening tasks revealed a mostly left-hemispheric cortical network. Areas showing consistent activity patterns included not only areas previously implicated in higher-level language processing, such as left prefrontal, superior and middle temporal areas, and anterior temporal lobe, but also parts of the control network as well as subcentral and more posterior temporal-parietal areas. Activity in this supramodal sentence-processing network starts in temporal areas and rapidly spreads to the other regions involved. The findings indicate not only the involvement of a large network of brain areas in supramodal language processing but also that the linguistic information contained in the unfolding sentences modulates brain activity in a word-specific manner across subjects. SIGNIFICANCE STATEMENT The brain can extract meaning from written and spoken messages alike. This requires activity of both brain circuits capable of processing sensory modality-specific aspects of the input signals as well as coordinated brain activity to extract modality-independent meaning from the input. Using traditional methods, it is difficult to disentangle modality-specific activation from modality-independent activation. In this work, we developed and applied a multivariate methodology that allows for a direct quantification of sensory modality-independent brain activity, revealing fast activation of a wide network of brain areas, both including and extending beyond the core network for language., (Copyright © 2020 the authors.)

Pre-stimulus alpha (8-12 Hz) and beta (16-20 Hz) oscillations have been frequently linked to the prediction of upcoming sensory input. Do these frequency bands serve as a neural marker of linguistic prediction as well? We hypothesized that if pre-stimulus alpha and beta oscillations index language predictions, their power should monotonically relate to the degree of predictability of incoming words based on past context. We expected that the more predictable the last word of a sentence, the stronger the alpha and beta power modulation. To test this, we measured neural responses with magnetoencephalography of healthy individuals during exposure to a set of linguistically matched sentences featuring three levels of sentence context constraint (high, medium and low constraint). We observed fluctuations in alpha and beta power before last word onset, and modulations in M400 amplitude after last word onset. The M400 amplitude was monotonically related to the degree of context constraint, with a high constraining context resulting in the strongest amplitude decrease. In contrast, pre-stimulus alpha and beta power decreased more strongly for intermediate constraints, followed by high and low constraints. Therefore, unlike the M400, pre-stimulus alpha and beta dynamics were not indexing the degree of word predictability from sentence context.

Autism spectrum disorder is increasingly associated with atypical perceptual and sensory symptoms. Here we explore the hypothesis that aberrant sensory processing in autism spectrum disorder could be linked to atypical intra- (local) and interregional (global) brain connectivity. To elucidate oscillatory dynamics and connectivity in the visual domain we used magnetoencephalography and a simple visual grating paradigm with a group of 18 adolescent autistic participants and 18 typically developing control subjects. Both groups showed similar increases in gamma (40-80 Hz) and decreases in alpha (8-13 Hz) frequency power in occipital cortex. However, systematic group differences emerged when analysing intra- and interregional connectivity in detail. First, directed connectivity was estimated using non-parametric Granger causality between visual areas V1 and V4. Feedforward V1-to-V4 connectivity, mediated by gamma oscillations, was equivalent between autism spectrum disorder and control groups, but importantly, feedback V4-to-V1 connectivity, mediated by alpha (8-13 Hz) oscillations, was significantly reduced in the autism spectrum disorder group. This reduction was positively correlated with autistic quotient scores, consistent with an atypical visual hierarchy in autism, characterized by reduced top-down modulation of visual input via alpha-band oscillations. Second, at the local level in V1, coupling of alpha-phase to gamma amplitude (alpha-gamma phase amplitude coupling) was reduced in the autism spectrum disorder group. This implies dysregulated local visual processing, with gamma oscillations decoupled from patterns of wider alpha-band phase synchrony (i.e. reduced phase amplitude coupling), possibly due to an excitation-inhibition imbalance. More generally, these results are in agreement with predictive coding accounts of neurotypical perception and indicate that visual processes in autism are less modulated by contextual feedback information., (© The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain.)

The brain's remarkable capacity to process spoken language virtually in real time requires fast and efficient information processing machinery. In this study, we investigated how frequency-specific brain dynamics relate to models of probabilistic language prediction during auditory narrative comprehension. We recorded MEG activity while participants were listening to auditory stories in Dutch. Using trigram statistical language models, we estimated for every word in a story its conditional probability of occurrence. On the basis of word probabilities, we computed how unexpected the current word is given its context (word perplexity) and how (un)predictable the current linguistic context is (word entropy). We then evaluated whether source-reconstructed MEG oscillations at different frequency bands are modulated as a function of these language processing metrics. We show that theta-band source dynamics are increased in high relative to low entropy states, likely reflecting lexical computations. Beta-band dynamics are increased in situations of low word entropy and perplexity possibly reflecting maintenance of ongoing cognitive context. These findings lend support to the idea that the brain engages in the active generation and evaluation of predicted language based on the statistical properties of the input signal., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

The way the human brain represents speech in memory is still unknown. An obvious characteristic of speech is its evolvement over time. During speech processing, neural oscillations are modulated by the temporal properties of the acoustic speech signal, but also acquired knowledge on the temporal structure of language influences speech perception-related brain activity. This suggests that speech could be represented in the temporal domain, a form of representation that the brain also uses to encode autobiographic memories. Empirical evidence for such a memory code is lacking. We investigated the nature of speech memory representations using direct cortical recordings in the left perisylvian cortex during delayed sentence reproduction in female and male patients undergoing awake tumor surgery. Our results reveal that the brain endogenously represents speech in the temporal domain. Temporal pattern similarity analyses revealed that the phase of frontotemporal low-frequency oscillations, primarily in the beta range, represents sentence identity in working memory. The positive relationship between beta power during working memory and task performance suggests that working memory representations benefit from increased phase separation. SIGNIFICANCE STATEMENT Memory is an endogenous source of information based on experience. While neural oscillations encode autobiographic memories in the temporal domain, little is known on their contribution to memory representations of human speech. Our electrocortical recordings in participants who maintain sentences in memory identify the phase of left frontotemporal beta oscillations as the most prominent information carrier of sentence identity. These observations provide evidence for a theoretical model on speech memory representations and explain why interfering with beta oscillations in the left inferior frontal cortex diminishes verbal working memory capacity. The lack of sentence identity coding at the syllabic rate suggests that sentences are represented in memory in a more abstract form compared with speech coding during speech perception and production., (Copyright © 2019 the authors.)

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This dataset, colloquially known as the Mother Of Unification Studies (MOUS) dataset, contains multimodal neuroimaging data that has been acquired from 204 healthy human subjects. The neuroimaging protocol consisted of magnetic resonance imaging (MRI) to derive information at high spatial resolution about brain anatomy and structural connections, and functional data during task, and at rest. In addition, magnetoencephalography (MEG) was used to obtain high temporal resolution electrophysiological measurements during task, and at rest. All subjects performed a language task, during which they processed linguistic utterances that either consisted of normal or scrambled sentences. Half of the subjects were reading the stimuli, the other half listened to the stimuli. The resting state measurements consisted of 5 minutes eyes-open for the MEG and 7 minutes eyes-closed for fMRI. The neuroimaging data, as well as the information about the experimental events are shared according to the Brain Imaging Data Structure (BIDS) format. This unprecedented neuroimaging language data collection allows for the investigation of various aspects of the neurobiological correlates of language.

The efficiency of neuronal information transfer in activated brain networks may affect behavioral performance. Gamma-band synchronization has been proposed to be a mechanism that facilitates neuronal processing of behaviorally relevant stimuli. In line with this, it has been shown that strong gamma-band activity in visual cortical areas leads to faster responses to a visual go cue. We investigated whether there are directly observable consequences of trial-by-trial fluctuations in non-invasively observed gamma-band activity on the neuronal response. Specifically, we hypothesized that the amplitude of the visual evoked response to a go cue can be predicted by gamma power in the visual system, in the window preceding the evoked response. Thirty-three human subjects (22 female) performed a visual speeded response task while their magnetoencephalogram (MEG) was recorded. The participants had to respond to a pattern reversal of a concentric moving grating. We estimated single trial stimulus-induced visual cortical gamma power, and correlated this with the estimated single trial amplitude of the most prominent event-related field (ERF) peak within the first 100 ms after the pattern reversal. In parieto-occipital cortical areas, the amplitude of the ERF correlated positively with gamma power, and correlated negatively with reaction times. No effects were observed for the alpha and beta frequency bands, despite clear stimulus onset induced modulation at those frequencies. These results support a mechanistic model, in which gamma-band synchronization enhances the neuronal gain to relevant visual input, thus leading to more efficient downstream processing and to faster responses., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Human language processing involves combinatorial operations that make human communication stand out in the animal kingdom. These operations rely on a dynamic interplay between the inferior frontal and the posterior temporal cortices. Using source reconstructed magnetoencephalography, we tracked language processing in the brain, in order to investigate how individual words are interpreted when part of sentence context. The large sample size in this study (n = 68) allowed us to assess how event-related activity is associated across distinct cortical areas, by means of inter-areal co-modulation within an individual. We showed that, within 500 ms of seeing a word, the word's lexical information has been retrieved and unified with the sentence context. This does not happen in a strictly feed-forward manner, but by means of co-modulation between the left posterior temporal cortex (LPTC) and left inferior frontal cortex (LIFC), for each individual word. The co-modulation of LIFC and LPTC occurs around 400 ms after the onset of each word, across the progression of a sentence. Moreover, these core language areas are supported early on by the attentional network. The results provide a detailed description of the temporal orchestration related to single word processing in the context of ongoing language., (Copyright © 2018. Published by Elsevier Inc.)

Representations learned by deep convolutional neural networks (CNNs) for object recognition are a widely investigated model of the processing hierarchy in the human visual system. Using functional magnetic resonance imaging, CNN representations of visual stimuli have previously been shown to correspond to processing stages in the ventral and dorsal streams of the visual system. Whether this correspondence between models and brain signals also holds for activity acquired at high temporal resolution has been explored less exhaustively. Here, we addressed this question by combining CNN-based encoding models with magnetoencephalography (MEG). Human participants passively viewed 1,000 images of objects while MEG signals were acquired. We modelled their high temporal resolution source-reconstructed cortical activity with CNNs, and observed a feed-forward sweep across the visual hierarchy between 75 and 200 ms after stimulus onset. This spatiotemporal cascade was captured by the network layer representations, where the increasingly abstract stimulus representation in the hierarchical network model was reflected in different parts of the visual cortex, following the visual ventral stream. We further validated the accuracy of our encoding model by decoding stimulus identity in a left-out validation set of viewed objects, achieving state-of-the-art decoding accuracy., (Copyright © 2017 Elsevier Inc. All rights reserved.)

The auditory steady state evoked response (ASSR) is a robust and frequently utilized phenomenon in psychophysiological research. It reflects the auditory cortical response to an amplitude-modulated constant carrier frequency signal. The present report provides a concrete example of a group analysis of the EEG data from 29 healthy human participants, recorded during an ASSR paradigm, using the FieldTrip toolbox. First, we demonstrate sensor-level analysis in the time domain, allowing for a description of the event-related potentials (ERPs), as well as their statistical evaluation. Second, frequency analysis is applied to describe the spectral characteristics of the ASSR, followed by group level statistical analysis in the frequency domain. Third, we show how time- and frequency-domain analysis approaches can be combined in order to describe the temporal and spectral development of the ASSR. Finally, we demonstrate source reconstruction techniques to characterize the primary neural generators of the ASSR. Throughout, we pay special attention to explaining the design of the analysis pipeline for single subjects and for the group level analysis. The pipeline presented here can be adjusted to accommodate other experimental paradigms and may serve as a template for similar analyses.

Speaking is a complex motor skill which requires near instantaneous integration of sensory and motor-related information. Current theory hypothesizes a complex interplay between motor and auditory processes during speech production, involving the online comparison of the speech output with an internally generated forward model. To examine the neural correlates of this intricate interplay between sensory and motor processes, the current study uses altered auditory feedback (AAF) in combination with magnetoencephalography (MEG). Participants vocalized the vowel/e/and heard auditory feedback that was temporarily pitch-shifted by only 25 cents, while neural activity was recorded with MEG. As a control condition, participants also heard the recordings of the same auditory feedback that they heard in the first half of the experiment, now without vocalizing. The participants were not aware of any perturbation of the auditory feedback. We found auditory cortical areas responded more strongly to the pitch shifts during vocalization. In addition, auditory feedback perturbation resulted in spectral power increases in the θ and lower β bands, predominantly in sensorimotor areas. These results are in line with current models of speech production, suggesting auditory cortical areas are involved in an active comparison between a forward model's prediction and the actual sensory input. Subsequently, these areas interact with motor areas to generate a motor response. Furthermore, the results suggest that θ and β power increases support auditory-motor interaction, motor error detection and/or sensory prediction processing., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Oscillatory activity in the alpha and gamma bands is considered key in shaping functional brain architecture. Power increases in the high-frequency gamma band are typically reported in parallel to decreases in the low-frequency alpha band. However, their functional significance and in particular their interactions are not well understood. The present study shows that, in the context of an N-back working memory task, alpha power decreases in the dorsal visual stream are related to gamma power increases in early visual areas. Granger causality analysis revealed directed interregional interactions from dorsal to ventral stream areas, in accordance with task demands. Present results reveal a robust, behaviorally relevant, and architectonically decisive power-to-power relationship between alpha and gamma activity. This relationship suggests that anatomically distant power fluctuations in oscillatory activity can link cerebral network dynamics on trial-by-trial basis during cognitive operations such as working memory., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Prior knowledge about the visual world can change how a visual stimulus is processed. Two forms of prior knowledge are often distinguished: stimulus familiarity (i.e., whether a stimulus has been seen before) and stimulus expectation (i.e., whether a stimulus is expected to occur, based on the context). Neurophysiological studies in monkeys have shown suppression of spiking activity both for expected and for familiar items in object-selective inferotemporal cortex. It is an open question, however, if and how these types of knowledge interact in their modulatory effects on the sensory response. To address this issue and to examine whether previous findings generalize to noninvasively measured neural activity in humans, we separately manipulated stimulus familiarity and expectation while noninvasively recording human brain activity using magnetoencephalography. We observed independent suppression of neural activity by familiarity and expectation, specifically in the lateral occipital complex, the putative human homologue of monkey inferotemporal cortex. Familiarity also led to sharpened response dynamics, which was predominantly observed in early visual cortex. Together, these results show that distinct types of sensory knowledge jointly determine the amount of neural resources dedicated to object processing in the visual ventral stream.

Human intracranial electroencephalography (iEEG) recordings provide data with much greater spatiotemporal precision than is possible from data obtained using scalp EEG, magnetoencephalography (MEG), or functional MRI. Until recently, the fusion of anatomical data (MRI and computed tomography (CT) images) with electrophysiological data and their subsequent analysis have required the use of technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that enables complex raw human iEEG data to be converted into more readily comprehensible illustrative representations. The protocol uses an open-source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and used by a large research community. In this protocol, we describe how to analyze complex iEEG datasets by providing an intuitive and rapid approach that can handle both neuroanatomical information and large electrophysiological datasets. We provide a worked example using an example dataset. We also explain how to automate the protocol and adjust the settings to enable analysis of iEEG datasets with other characteristics. The protocol can be implemented by a graduate student or postdoctoral fellow with minimal MATLAB experience and takes approximately an hour to execute, excluding the automated cortical surface extraction.

We present a significant extension of the Brain Imaging Data Structure (BIDS) to support the specific aspects of magnetoencephalography (MEG) data. MEG measures brain activity with millisecond temporal resolution and unique source imaging capabilities. So far, BIDS was a solution to organise magnetic resonance imaging (MRI) data. The nature and acquisition parameters of MRI and MEG data are strongly dissimilar. Although there is no standard data format for MEG, we propose MEG-BIDS as a principled solution to store, organise, process and share the multidimensional data volumes produced by the modality. The standard also includes well-defined metadata, to facilitate future data harmonisation and sharing efforts. This responds to unmet needs from the multimodal neuroimaging community and paves the way to further integration of other techniques in electrophysiology. MEG-BIDS builds on MRI-BIDS, extending BIDS to a multimodal data structure. We feature several data-analytics software that have adopted MEG-BIDS, and a diverse sample of open MEG-BIDS data resources available to everyone.

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., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Reinstatement of memory-related neural activity measured with high temporal precision potentially provides a useful index for real-time monitoring of the timing of activation of memory content during cognitive processing. The utility of such an index extends to any situation where one is interested in the (relative) timing of activation of different sources of information in memory, a paradigm case of which is tracking lexical activation during language processing. Essential for this approach is that memory reinstatement effects are robust, so that their absence (in the average) definitively indicates that no lexical activation is present. We used electroencephalography to test the robustness of a reported subsequent memory finding involving reinstatement of frequency-specific entrained oscillatory brain activity during subsequent recognition. Participants learned lists of words presented on a background flickering at either 6 or 15 Hz to entrain a steady-state brain response. Target words subsequently presented on a non-flickering background that were correctly identified as previously seen exhibited reinstatement effects at both entrainment frequencies. Reliability of these statistical inferences was however critically dependent on the approach used for multiple comparisons correction. We conclude that effects are not robust enough to be used as a reliable index of lexical activation during language processing.

Like many other types of memory formation, novel word learning benefits from an offline consolidation period after the initial encoding phase. A previous EEG study has shown that retrieval of novel words elicited more word-like-induced electrophysiological brain activity in the theta band after consolidation [Bakker, I., Takashima, A., van Hell, J. G., Janzen, G., & McQueen, J. M. Changes in theta and beta oscillations as signatures of novel word consolidation. Journal of Cognitive Neuroscience, 27, 1286-1297, 2015]. This suggests that theta-band oscillations play a role in lexicalization, but it has not been demonstrated that this effect is directly caused by the formation of lexical representations. This study used magnetoencephalography to localize the theta consolidation effect to the left posterior middle temporal gyrus (pMTG), a region known to be involved in lexical storage. Both untrained novel words and words learned immediately before test elicited lower theta power during retrieval than existing words in this region. After a 24-hr consolidation period, the difference between novel and existing words decreased significantly, most strongly in the left pMTG. The magnitude of the decrease after consolidation correlated with an increase in behavioral competition effects between novel words and existing words with similar spelling, reflecting functional integration into the mental lexicon. These results thus provide new evidence that consolidation aids the development of lexical representations mediated by the left pMTG. Theta synchronization may enable lexical access by facilitating the simultaneous activation of distributed semantic, phonological, and orthographic representations that are bound together in the pMTG.

Objective: To investigate the possibility of tremor detection based on deep brain activity., Methods: We re-analyzed recordings of local field potentials (LFPs) from the subthalamic nucleus in 10 PD patients (12 body sides) with spontaneously fluctuating rest tremor. Power in several frequency bands was estimated and used as input to Hidden Markov Models (HMMs) which classified short data segments as either tremor-free rest or rest tremor. HMMs were compared to direct threshold application to individual power features., Results: Applying a threshold directly to band-limited power was insufficient for tremor detection (mean area under the curve [AUC] of receiver operating characteristic: 0.64, STD: 0.19). Multi-feature HMMs, in contrast, allowed for accurate detection (mean AUC: 0.82, STD: 0.15), using four power features obtained from a single contact pair. Within-patient training yielded better accuracy than across-patient training (0.84vs. 0.78, p=0.03), yet tremor could often be detected accurately with either approach. High frequency oscillations (>200Hz) were the best performing individual feature., Conclusions: LFP-based markers of tremor are robust enough to allow for accurate tremor detection in short data segments, provided that appropriate statistical models are used., Significance: LFP-based markers of tremor could be useful control signals for closed-loop deep brain stimulation., (Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

The brain's remarkable capacity for language requires bidirectional interactions between functionally specialized brain regions. We used magnetoencephalography to investigate interregional interactions in the brain network for language while 102 participants were reading sentences. Using Granger causality analysis, we identified inferior frontal cortex and anterior temporal regions to receive widespread input and middle temporal regions to send widespread output. This fits well with the notion that these regions play a central role in language processing. Characterization of the functional topology of this network, using data-driven matrix factorization, which allowed for partitioning into a set of subnetworks, revealed directed connections at distinct frequencies of interaction. Connections originating from temporal regions peaked at alpha frequency, whereas connections originating from frontal and parietal regions peaked at beta frequency. These findings indicate that the information flow between language-relevant brain areas, which is required for linguistic processing, may depend on the contributions of distinct brain rhythms., Competing Interests: The authors declare no conflict of interest.

Accumulating evidence suggests that spoken word production requires different amounts of top-down control depending on the prevailing circumstances. For example, during Stroop-like tasks, the interference in response time (RT) is typically larger following congruent trials than following incongruent trials. This effect is called the Gratton effect, and has been taken to reflect top-down control adjustments based on the previous trial type. Such control adjustments have been studied extensively in Stroop and Eriksen flanker tasks (mostly using manual responses), but not in the picture-word interference (PWI) task, which is a workhorse of language production research. In one of the few studies of the Gratton effect in PWI, Van Maanen and Van Rijn (2010) examined the effect in picture naming RTs during dual-task performance. Based on PWI effect differences between dual-task conditions, they argued that the functional locus of the PWI effect differs between post-congruent trials (i.e., locus in perceptual and conceptual encoding) and post-incongruent trials (i.e., locus in word planning). However, the dual-task procedure may have contaminated the results. We therefore performed an electroencephalography (EEG) study on the Gratton effect in a regular PWI task. We observed a PWI effect in the RTs, in the N400 component of the event-related brain potentials, and in the midfrontal theta power, regardless of the previous trial type. Moreover, the RTs, N400, and theta power reflected the Gratton effect. These results provide evidence that the PWI effect arises at the word planning stage following both congruent and incongruent trials, while the amount of top-down control changes depending on the previous trial type., (Copyright © 2017 Elsevier Ltd. All rights reserved.)