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18 mei 2023, Item does not contain fulltext, 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.

23 mei 2023, Item does not contain fulltext, 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 towards 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 around 100 milliseconds in right frontal areas. We conclude that low-frequency responses to words are changed by sentential context. The results of this study speak to 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

22 juni 2023, Item does not contain fulltext, 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.

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Contains fulltext : 299409.pdf (Publisher’s version ) (Closed access), 1 p.

Contains fulltext : 246921.pdf (Publisher’s version ) (Open Access), 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.

Radboud University, 11 februari 2022, Promotor : Hagoort, P. Co-promotores : Schoffelen, J.M., Rabovsky, M., Contains fulltext : 245927.pdf (Publisher’s version ) (Open Access)

Item does not contain fulltext, 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.

Contains fulltext : 240461.pdf (Publisher’s version ) (Open Access), 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-byword 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 (<150ms) and late stages of word processing, but also interact during late stages of word processing (>150-250ms), thus helping to converge previous contradictory eye-tracking and electrophysiological literature. Current neuro-cognitive models of reading would benefit from accounting for these differing effects of lexical frequency and predictability on different stages of word processing.

Item does not contain fulltext, 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.

Item does not contain fulltext, Theorists propose that the brain constantly generates implicit predictions that guide information processing. During language comprehension, such predictions have indeed been observed, but it remains disputed under which conditions and at which processing level these predictions occur. Here, we address both questions by analyzing brain recordings of participants listening to audiobooks, and using a deep neural network to quantify the predictions evoked by the story. We find that brain responses are continuously modulated by linguistic predictions. We observe predictions at the level of meaning, grammar, words, and speech sounds, and find that high-level predictions can inform low-level ones. These results establish the predictive nature of language processing, demonstrating that the brain spontaneously predicts upcoming language at multiple levels of abstraction. 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 spo

Contains fulltext : 282543.pdf (Publisher’s version ) (Open Access), 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

Contains fulltext : 252472.pdf (Publisher’s version ) (Open Access)

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Contains fulltext : 245429.pdf (Publisher’s version ) (Open Access)

Radboud University, 30 maart 2021, Promotor : Hagoort, P. Co-promotor : Schoffelen, J.M., Item does not contain fulltext, In contextually rich language comprehension settings listeners can rely on past context and to generate predictions about the upcoming words. Neuroscientific theories propose that some cognitive processes related to prediction might be observed in the brain as neuronal oscillations -- rhythmic firing of a large pool of neurons. To perform such feats of prediction, neural circuits must implement some form of processing memory to maintain the past information and use it while processing the new input. In this thesis, we investigated aspects of the neurobiology of language in naturalistic language comprehension using magnetoencephalography (MEG) and computational models of prediction and memory. We begin by reviewing the use of computational linguistic techniques in cognitive neuroscience. In chapter 2, we report an MEG study on oscillatory predictive processing. We show that slow theta-band dynamics are increased in unpredictable contexts, likely reflecting lexical computations, and that faster beta-band dynamics are increased in more predictable contexts possibly reflecting context maintenance. In chapter 3, we describe an MEG dataset for building artificial neural networks of brain dynamics. We provide a brief validation analysis showing accurate and robust localization of MEG dynamics in primary auditory areas. In chapter 4, we used recurrent neural networks (RNNs) to model MEG dynamics on dataset from chapter 3. We show that RNNs reliably predict the MEG signal in the known higher-level areas of the language network. In chapter 5, we investigated a neuronal substrate for memory: neuronal spike-rate adaptation. We show that making neurons more adaptive resulted in progressively higher performance in a working memory task. The results suggest neuronal adaptation plays an important role as memory mechanism in language. In conclusion, we briefly discuss on the potential challenges of model-based approaches in cognitive neuroscience of language and call for a stronger

Item does not contain fulltext, Alpha activity (8-14 Hz) is the dominant rhythm in the awake brain, and thought to play an important role in setting the brain’s internal state. 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, the consequences are for subsequent perception. Here, human subjects (male and female) performed a visual detection task in which we manipulated their decision criteria in a block-wise manner. While our manipulation led to substantial criterion shifts, these shifts were not reflected in pre-stimulus alpha-band changes. Rather, lower pre-stimulus 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.

Contains fulltext : 228929.pdf (publisher's version ) (Open Access), 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 ta

Radboud University, 17 december 2020, Promotor : Hagoort, P. Co-promotor : Schoffelen, J.M., Contains fulltext : 227394.pdf (publisher's version ) (Open Access), The brain is capable of responding to the environment dynamically and thus producing the appropriate behavior. This happens for example by changing the physical connections in the brain, but on a short time scale especially by tuning the relevant brain areas to each other through brain waves. I investigated how brain waves affect local brain activity and how this changes behavior. I did this by letting volunteers do computer tasks while recording brain activity with an MEG device. For example, they had to respond to a changing image. I showed how the strength of the characteristic brain signal of that image depends on the presence of fast brain waves. If the presence of fast brain waves was high in the visual brain area, the characteristic brain signal was stronger and people reacted faster to it. This shows that fast brain waves are related to the efficiency of the brain process.

Contains fulltext : 218355.pdf (Publisher’s version ) (Open Access), 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.

21 september 2020, Contains fulltext : 224765.pdf (Publisher’s version ) (Closed access), 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.

Contains fulltext : 221842.pdf (publisher's version ) (Open Access), 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.

Contains fulltext : 217515.pdf (publisher's version ) (Open Access), 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 include not only areas previously implicated in higher-level language processing, such as left prefrontal, superior & 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 do not only indicate the involvement of a large network of brain areas in supramodal language processing, but also indicate that the linguistic information contained in the unfolding sentences modulates brain activity in a word-specific manner across subjects.

15 december 2020, Contains fulltext : 228492.pdf (publisher's version ) (Open Access), 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.

Contains fulltext : 218379.pdf (publisher's version ) (Open Access), 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.

Contains fulltext : 221897.pdf (publisher's version ) (Open Access), 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.

Contains fulltext : 218382.pdf (publisher's version ) (Open Access), 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.

Contains fulltext : 205266.pdf (publisher's version ) (Open Access)

The Eleventh Annual Society for the Neurobiology of Language Meeting (SNL 2019) (Helsinki, Finland, August 20-22, 2019), Item does not contain fulltext

Contains fulltext : 205952.pdf (publisher's version ) (Open Access), 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.

Contains fulltext : 198200.pdf (Publisher’s version ) (Closed access), 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.

Contains fulltext : 202334.pdf (publisher's version ) (Open Access), 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.

Contains fulltext : 203088.pdf (publisher's version ) (Open Access), In the original version of this Data Descriptor, the author Annika Hultén was listed incorrectly as being affiliated with NatMEG, Karolinska Institutet, Stockholm, Sweden. This has been corrected in both the HTML and PDF versions to Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.

The Eleventh Annual Society for the Neurobiology of Language Meeting (SNL 2019) (Helsinki, Finland, August 20-22, 2019), Item does not contain fulltext

Contains fulltext : 205954.pdf (publisher's version ) (Open Access), 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

Item does not contain fulltext, Interactions between functionally specialized brain regions are crucial for normal brain function. Magnetoencephalography (MEG) is suited to capture these interactions because it provides whole head measurements of brain activity with temporal resolution in the millisecond range. Many different measures of connectivity exist, and in order to take the connectivity analysis results at face value, one should be aware of the strengths and weaknesses of these measures. Next to this, an important challenge in MEG connectivity analysis lies in the fact that more than one sensor picks up the activity of any underlying source. This field spread severely limits the utility of connectivity measures computed directly between sensor recordings. As a consequence, neuronal interactions should be ideally studied on the level of the reconstructed sources. MEG is well suited for this purpose, since its signal properties and high spatial sampling allow for relatively accurate unmixing of the sensor recordings. This chapter provides some necessary background on connectivity analysis in general and proceeds by describing the challenges that are associated with the analysis of MEG-based connectivity at the sensor level. Source-level approaches are described, and some recent advances with respect to MEG-based connectivity during the resting state and graph theoretic approaches are described.

Contains fulltext : 201121.pdf (publisher's version ) (Open Access), 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.

Contains fulltext : 192367.pdf (publisher's version ) (Open Access), 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.

Contains fulltext : 195998.pdf (publisher's version ) (Open Access), The auditory steady state evoked response (ASSR) is a robust and frequently utilized phenomenon in psychophysiological research. It reflects the auditory cortical response to a frequency modulated auditory stimulus. 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.

Contains fulltext : 195013.pdf (publisher's version ) (Open Access), 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.

Contains fulltext : 191404.pdf (publisher's version ) (Open Access)

Radboud University, 05 februari 2018, Promotores : Hagoort, P., McQueen, J.M. Co-promotores : Acheson, D.J., Schoffelen, J.M., Contains fulltext : 180052.pdf (publisher's version ) (Open Access)

Contains fulltext : 191685.pdf (publisher's version ) (Open Access), 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.

Neuroscience 2018: The 48th Annual Meeting of the Society for Neuroscience (San Diego, CA, 3-7 november 2018), Item does not contain fulltext, Grid cells in the rodent and human entorhinal cortex are a critical component of the brain’s spatial coding system. In virtual-reality (VR) navigation tasks in humans, the fMRI BOLD signal in the entorhinal cortex exhibits hexadirectional modulations that may reflect population activity of grid cells. However, it remains unknown whether and how grid cell population activity specifically gives rise to this hexadirectional hemodynamic fMRI signal. Here we address this issue in two steps. First, we employed a VR navigation experiment using magnetoencephalography (MEG) in human participants and found hexadirectional signal modulations in the high-gamma band, source-localised to the medial temporal lobe. Next, we conducted analyses to test the relationship between grid cell activity and local field potential (LFP) recordings in freely moving rats. We found hexadirectional modulations in the same frequency band as in the human MEG navigation experiment. The orientation of this hexadirectional LFP modulation was aligned to the orientation of the hexagonally symmetric firing patterns of grid cells. Together, these findings describe new ways to measure grid cell population activity and their non-invasive source localisation using MEG. Crucially, we link grid cell activity to measures of population activity in rats and humans, thereby elucidating the physiological basis of non-invasive grid cell population measures previously revealed with fMRI. Since grid cell function is affected early in Alzheimer ’s disease, understanding how to measure their activity with non-invasive methods is of high clinical relevance.