Oscillatory dynamics of cortical functional connections in semantic prediction

Parameterization of the Differences in Neural Oscillations Recorded by Wearable Magnetoencephalography for Chinese Semantic Cognition

Neural oscillations observed during semantic processing embody the function of brain language processing. Precise parameterization of the differences in these oscillations across various semantics from a time-frequency perspective is pivotal for elucidating the mechanisms of brain language processing. The superlet transform and cluster depth test were used to compute the time-frequency representation of oscillatory difference (ODTFR) between neural activities recorded by optically pumped magnetometer-based magnetoencephalography (OPM-MEG) during processing congruent and incongruent Chinese semantics. Subsequently, ODTFR was parameterized based on the definition of local events. Finally, this study calculated the specific time-frequency values at which oscillation differences occurred in multiple auditory-language-processing regions. It was found that these oscillatory differences appeared in most regions and were mainly concentrated in the beta band. The average peak frequency of these oscillatory differences was 15.7 Hz, and the average peak time was 457 ms. These findings offer a fresh perspective on the neural mechanisms underlying the processing of distinct Chinese semantics and provide references and insights for analyzing language-related brain activities recorded by OPM-MEG.

Chronic Cannabis users exhibit altered oscillatory dynamics and functional connectivity serving visuospatial processing

BACKGROUND

Cannabis is the most widely used psychoactive drug in the United States. While multiple studies have associated acute cannabis consumption with alterations in cognitive function (e.g., visual and spatial attention), far less is known regarding the effects of chronic consumption on the neural dynamics supporting these cognitive functions.

METHODS

We used magnetoencephalography (MEG) and an established visuospatial processing task to elicit multi-spectral neuronal responses in 44 regular cannabis users and 53 demographically matched non-user controls. To examine the effects of chronic cannabis use on the oscillatory dynamics underlying visuospatial processing, neural responses were imaged using a time-frequency resolved beamformer and compared across groups.

RESULTS

Neuronal oscillations serving visuospatial processing were identified in the theta (4-8 Hz), alpha (8-14 Hz), and gamma range (56-76 Hz), and these were imaged and examined for group differences. Our key results indicated that users exhibited weaker theta oscillations in occipital and cerebellar regions and weaker gamma responses in the left temporal cortices compared to non-users. Lastly, alpha oscillations did not differ, but alpha connectivity among higher-order attention areas was weaker in cannabis users relative to non-users and correlated with performance.

CONCLUSIONS

Overall, these results suggest that chronic cannabis users have alterations in the oscillatory dynamics and neural connectivity serving visuospatial attention. Such alterations were observed across multiple cortical areas critical for higher-order processing and may reflect compensatory activity and/or the initial emergence of aberrant dynamics. Future work is needed to fully understand the implications of altered multispectral oscillations and neural connectivity in cannabis users.

Characterizing directional dynamics of semantic prediction based on inter-regional temporal generalization

The event-related potential/field component N400(m) has been widely used as a neural index for semantic prediction. It has long been hypothesized that feedback information from inferior frontal areas plays a critical role in generating the N400. However, due to limitations in causal connectivity estimation, direct testing of this hypothesis has remained difficult. Here, magnetoencephalography (MEG) data was obtained during a classic N400 paradigm where the semantic predictability of a fixed target noun was manipulated in simple German sentences. To estimate causality, we implemented a novel approach based on machine learning and temporal generalization to estimate the effect of inferior frontal gyrus (IFG) on temporal areas. In this method, a support vector machine (SVM) classifier is trained on each time point of the neural activity in IFG to classify less predicted (LP) and highly predicted (HP) nouns and then tested on all time points of superior/middle temporal sub-regions activity (and vice versa, to establish spatio-temporal evidence for or against causality). The decoding accuracy was significantly above chance level when the classifier was trained on IFG activity and tested on future activity in superior and middle temporal gyrus (STG/MTG). The results present new evidence for a model predictive speech comprehension where predictive IFG activity is fed back to shape subsequent activity in STG/MTG, implying a feedback mechanism in N400 generation. In combination with the also observed strong feedforward effect from left STG/MTG to IFG, our findings provide evidence of dynamic feedback and feedforward influences between IFG and temporal areas during N400 generation.

Predictive coding across the left fronto-temporal hierarchy during language comprehension

We used magnetoencephalography (MEG) and event-related potentials (ERPs) to track the time-course and localization of evoked activity produced by expected, unexpected plausible, and implausible words during incremental language comprehension. We suggest that the full pattern of results can be explained within a hierarchical predictive coding framework in which increased evoked activity reflects the activation of residual information that was not already represented at a given level of the fronto-temporal hierarchy ("error" activity). Between 300 and 500 ms, the three conditions produced progressively larger responses within left temporal cortex (lexico-semantic prediction error), whereas implausible inputs produced a selectively enhanced response within inferior frontal cortex (prediction error at the level of the event model). Between 600 and 1,000 ms, unexpected plausible words activated left inferior frontal and middle temporal cortices (feedback activity that produced top-down error), whereas highly implausible inputs activated left inferior frontal cortex, posterior fusiform (unsuppressed orthographic prediction error/reprocessing), and medial temporal cortex (possibly supporting new learning). Therefore, predictive coding may provide a unifying theory that links language comprehension to other domains of cognition.

A deep hierarchy of predictions enables online meaning extraction in a computational model of human speech comprehension

Understanding speech requires mapping fleeting and often ambiguous soundwaves to meaning. While humans are known to exploit their capacity to contextualize to facilitate this process, how internal knowledge is deployed online remains an open question. Here, we present a model that extracts multiple levels of information from continuous speech online. The model applies linguistic and nonlinguistic knowledge to speech processing, by periodically generating top-down predictions and incorporating bottom-up incoming evidence in a nested temporal hierarchy. We show that a nonlinguistic context level provides semantic predictions informed by sensory inputs, which are crucial for disambiguating among multiple meanings of the same word. The explicit knowledge hierarchy of the model enables a more holistic account of the neurophysiological responses to speech compared to using lexical predictions generated by a neural network language model (GPT-2). We also show that hierarchical predictions reduce peripheral processing via minimizing uncertainty and prediction error. With this proof-of-concept model, we demonstrate that the deployment of hierarchical predictions is a possible strategy for the brain to dynamically utilize structured knowledge and make sense of the speech input.

Alpha oscillations in left perisylvian cortices support semantic processing and predict performance

Semantic processing is the ability to discern and maintain conceptual relationships among words and objects. While the neural circuits serving semantic representation and controlled retrieval are well established, the neuronal dynamics underlying these processes are poorly understood. Herein, we examined 25 healthy young adults who completed a semantic relation word-matching task during magnetoencephalography (MEG). MEG data were examined in the time-frequency domain and significant oscillatory responses were imaged using a beamformer. Whole-brain statistical analyses were conducted to compare semantic-related to length-related neural oscillatory responses. Time series were extracted to visualize the dynamics and were linked to task performance using structural equation modeling. The results indicated that participants had significantly longer reaction times in semantic compared to length trials. Robust MEG responses in the theta (3-6 Hz), alpha (10-16 Hz), and gamma (64-76 Hz and 64-94 Hz) bands were observed in parieto-occipital and frontal cortices. Whole-brain analyses revealed stronger alpha oscillations in a left-lateralized network during semantically related relative to length trials. Importantly, stronger alpha oscillations in the left superior temporal gyrus during semantic trials predicted faster responses. These data reinforce existing literature and add novel temporal evidence supporting the executive role of the semantic control network in behavior.

Synchronization patterns reveal neuronal coding of working memory content

Neuronal oscillations are suggested to play an important role in auditory working memory (WM), but their contribution to content-specific representations has remained unclear. Here, we measure magnetoencephalography during a retro-cueing task with parametric ripple-sound stimuli, which are spectrotemporally similar to speech but resist non-auditory memory strategies. Using machine learning analyses, with rigorous between-subject cross-validation and non-parametric permutation testing, we show that memorized sound content is strongly represented in phase-synchronization patterns between subregions of auditory and frontoparietal cortices. These phase-synchronization patterns predict the memorized sound content steadily across the studied maintenance period. In addition to connectivity-based representations, there are indices of more local, “activity silent” representations in auditory cortices, where the decoding accuracy of WM content significantly increases after task-irrelevant “impulse stimuli.” Our results demonstrate that synchronization patterns across auditory sensory and association areas orchestrate neuronal coding of auditory WM content. This connectivity-based coding scheme could also extend beyond the auditory domain.

Mamashli et al. use machine learning analyses of human magnetoencephalography (MEG) recordings to study “working memory,” maintenance of information in mind over brief periods of time. Their results show that the human brain maintains working memory content in transient functional connectivity patterns across sensory and association areas.

Children with autism spectrum disorder show altered functional connectivity and abnormal maturation trajectories in response to inverted faces

The processing of information conveyed by faces is a critical component of social communication. While the neurophysiology of processing upright faces has been studied extensively in autism spectrum disorder (ASD), less is known about the neurophysiological abnormalities associated with processing inverted faces in ASD. We used magnetoencephalography (MEG) to study both long-range and local functional connectivity, with the latter assessed using local cross-frequency coupling, in response to inverted faces stimuli, in 7-18 years old individuals with ASD and age and IQ matched typically developing (TD) individuals. We found abnormally reduced coupling between the phase of the alpha rhythm and the amplitude of the gamma rhythm in the fusiform face area (FFA) in response to inverted faces, as well as reduced long-range functional connectivity between the FFA and the inferior frontal gyrus (IFG) in response to inverted faces in the ASD group. These group differences were absent in response to upright faces. The magnitude of functional connectivity between the FFA and the IFG was significantly correlated with the severity of ASD, and FFA-IFG long-range functional connectivity increased with age in TD group, but not in the ASD group. Our findings suggest that both local and long-range functional connectivity are abnormally reduced in children with ASD when processing inverted faces, and that the pattern of abnormalities associated with the processing of inverted faces differs from the pattern of upright faces in ASD, likely due to the presumed greater reliance on top-down regulations necessary for efficient processing of inverted faces. LAY SUMMARY: We found alterations in the neurophysiological responses to inverted faces in children with ASD, that were not reflected in the evoked responses, and were not observed in the responses to upright faces. These alterations included reduced local functional connectivity in the fusiform face area (FFA), and decreased long-range alpha-band modulated functional connectivity between the FFA and the left IFG. The magnitude of long-range functional connectivity between the FFA and the inferior frontal gyrus was correlated with the severity of ASD.

Random matrix theory for analysing the brain functional network in lower limb motor imagery

We use random matrix theory (RMT) to investigate the statistical properties of brain functional networks in lower limb motor imagery. Functional connectivity was calculated by Pearson correlation coefficient (PCC), mutual information (MTI) and phase locking value (PLV) extracted from EEG signals. We found that when the measured subjects imagined the movements of their lower limbs the spectral density as well as the level spacings displayed deviations from the random matrix prediction. In particular, a significant difference between the left and right foot imaginary movements was observed in the maximum eigenvalue from the PCC, which can provide a theoretical basis for further study on the classification of unilateral movement of lower limbs.

Distinct Regional Oscillatory Connectivity Patterns During Auditory Target and Novelty Processing

Auditory attention allows us to focus on relevant target sounds in the acoustic environment while maintaining the capability to orient to unpredictable (novel) sound changes. An open question is whether orienting to expected vs. unexpected auditory events are governed by anatomically distinct attention pathways, respectively, or by differing communication patterns within a common system. To address this question, we applied a recently developed PeSCAR analysis method to evaluate spectrotemporal functional connectivity patterns across subregions of broader cortical regions of interest (ROIs) to analyze magnetoencephalography data obtained during a cued auditory attention task. Subjects were instructed to detect a predictable harmonic target sound embedded among standard tones in one ear and to ignore the standard tones and occasional unpredictable novel sounds presented in the opposite ear. Phase coherence of estimated source activity was calculated between subregions of superior temporal, frontal, inferior parietal, and superior parietal cortex ROIs. Functional connectivity was stronger in response to target than novel stimuli between left superior temporal and left parietal ROIs and between left frontal and right parietal ROIs, with the largest effects observed in the beta band (15-35 Hz). In contrast, functional connectivity was stronger in response to novel than target stimuli in inter-hemispheric connections between left and right frontal ROIs, observed in early time windows in the alpha band (8-12 Hz). Our findings suggest that auditory processing of expected target vs. unexpected novel sounds involves different spatially, temporally, and spectrally distributed oscillatory connectivity patterns across temporal, parietal, and frontal areas.

Magnetoencephalography and Language

This article provides an overview of research that uses magnetoencephalography to understand the brain basis of human language. The cognitive processes and brain networks that have been implicated in written and spoken language comprehension and production are discussed in relation to different methodologies: we review event-related brain responses, research on the coupling of neural oscillations to speech, oscillatory coupling between brain regions (eg, auditory-motor coupling), and neural decoding approaches in naturalistic language comprehension.

The Relation between Alpha/Beta Oscillations and the Encoding of Sentence induced Contextual Information

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.

MVPA Analysis of Intertrial Phase Coherence of Neuromagnetic Responses to Words Reliably Classifies Multiple Levels of Language Processing in the Brain

Neural processing of language is still among the most poorly understood functions of the human brain, whereas a need to objectively assess the neurocognitive status of the language function in a participant-friendly and noninvasive fashion arises in various situations. Here, we propose a solution for this based on a short task-free recording of MEG responses to a set of spoken linguistic contrasts. We used spoken stimuli that diverged lexically (words/pseudowords), semantically (action-related/abstract), or morphosyntactically (grammatically correct/ungrammatical). Based on beamformer source reconstruction we investigated intertrial phase coherence (ITPC) in five canonical bands (α, β, and low, medium, and high γ) using multivariate pattern analysis (MVPA). Using this approach, we could successfully classify brain responses to meaningful words from meaningless pseudowords, correct from incorrect syntax, as well as semantic differences. The best classification results indicated distributed patterns of activity dominated by core temporofrontal language circuits and complemented by other areas. They varied between the different neurolinguistic properties across frequency bands, with lexical processes classified predominantly by broad γ, semantic distinctions by α and β, and syntax by low γ feature patterns. Crucially, all types of processing commenced in a near-parallel fashion from ∼100 ms after the auditory information allowed for disambiguating the spoken input. This shows that individual neurolinguistic processes take place simultaneously and involve overlapping yet distinct neuronal networks that operate at different frequency bands. This brings further hope that brain imaging can be used to assess neurolinguistic processes objectively and noninvasively in a range of populations.

Oscillatory dynamics of cortical functional connections in semantic prediction

An event related potential, known as the N400, has been particularly useful in investigating language processing as it serves as a neural index for semantic prediction. There are numerous studies on the functional segregation of N400 neural sources; however, the oscillatory dynamics of functional connections among the relevant sources has remained elusive. In this study we acquired magnetoencephalography data during a classic N400 paradigm, where the semantic predictability of a fixed target noun was manipulated in simple German sentences. We conducted inter-regional functional connectivity (FC) and time-frequency analysis on known regions of the semantic network, encompassing bilateral temporal, and prefrontal cortices. Increased FC was found in less predicted (LP) nouns compared with highly predicted (HP) nouns in three connections: (a) right inferior frontal gyrus (IFG) and right middle temporal gyrus (MTG) from 0 to 300 ms mainly within the alpha band, (b) left lateral orbitofrontal (LOF) and right IFG around 400 ms within the beta band, and (c) left superior temporal gyrus (STG) and left LOF from 300 to 700 ms in the beta and low gamma bands. Furthermore, gamma spectral power (31-70 Hz) was stronger in HP nouns than in LP nouns in left anterior temporal cortices in earlier time windows (0-200 ms). Our findings support recent theories in language comprehension, suggesting fronto-temporal top-down connections are mainly mediated through beta oscillations while gamma band frequencies are involved in matching between prediction and input.