MEG Responses Research Articles

Population receptive field models capture event-related MEG responses

Population receptive field models capture event-related MEG responses

Subword Representations Successfully Decode Brain Responses to Morphologically Complex Written Words

Abstract This study extends the idea of decoding word-evoked brain activations using a corpus-semantic vector space to multimorphemic words in the agglutinative Finnish language. The corpus-semantic models are trained on word segments, and decoding is carried out with word vectors that are composed of these segments. We tested several alternative vector-space models using different segmentations: no segmentation (whole word), linguistic morphemes, statistical morphemes, random segmentation, and character-level 1-, 2- and 3-grams, and paired them with recorded MEG responses to multimorphemic words in a visual word recognition task. For all variants, the decoding accuracy exceeded the standard word-label permutation-based significance thresholds at 350–500 ms after stimulus onset. However, the critical segment-label permutation test revealed that only those segmentations that were morphologically aware reached significance in the brain decoding task. The results suggest that both whole-word forms and morphemes are represented in the brain and show that neural decoding using corpus-semantic word representations derived from compositional subword segments is applicable also for multimorphemic word forms. This is especially relevant for languages with complex morphology, because a large proportion of word forms are rare and it can be difficult to find statistically reliable surface representations for them in any large corpus.

Target of selective auditory attention can be robustly followed with MEG

Selective auditory attention enables filtering of relevant acoustic information from irrelevant. Specific auditory responses, measurable by magneto- and electroencephalography (MEG/EEG), are known to be modulated by attention to the evoking stimuli. However, such attention effects have typically been studied in unnatural conditions (e.g. during dichotic listening of pure tones) and have been demonstrated mostly in averaged auditory evoked responses. To test how reliably we can detect the attention target from unaveraged brain responses, we recorded MEG data from 15 healthy subjects that were presented with two human speakers uttering continuously the words “Yes” and “No” in an interleaved manner. The subjects were asked to attend to one speaker. To investigate which temporal and spatial aspects of the responses carry the most information about the target of auditory attention, we performed spatially and temporally resolved classification of the unaveraged MEG responses using a support vector machine. Sensor-level decoding of the responses to attended vs. unattended words resulted in a mean accuracy of 79% pm 2 % (N = 14) for both stimulus words. The discriminating information was mostly available 200–400 ms after the stimulus onset. Spatially-resolved source-level decoding indicated that the most informative sources were in the auditory cortices, in both the left and right hemisphere. Our result corroborates attention modulation of auditory evoked responses and shows that such modulations are detectable in unaveraged MEG responses at high accuracy, which could be exploited e.g. in an intuitive brain–computer interface.

Objects sharpen visual scene representations: evidence from MEG decoding.

Real-world scenes consist of objects, defined by local information, and scene background, defined by global information. Although objects and scenes are processed in separate pathways in visual cortex, their processing interacts. Specifically, previous studies have shown that scene context makes blurry objects look sharper, an effect that can be observed as a sharpening of object representations in visual cortex from around 300 ms after stimulus onset. Here, we use MEG to show that objects can also sharpen scene representations, with the same temporal profile. Photographs of indoor (closed) and outdoor (open) scenes were blurred such that they were difficult to categorize on their own but easily disambiguated by the inclusion of an object. Classifiers were trained to distinguish MEG response patterns to intact indoor and outdoor scenes, presented in an independent run, and tested on degraded scenes in the main experiment. Results revealed better decoding of scenes with objects than scenes alone and objects alone from 300 ms after stimulus onset. This effect was strongest over left posterior sensors. These findings show that the influence of objects on scene representations occurs at similar latencies as the influence of scenes on object representations, in line with a common predictive processing mechanism.

Trials and tribulations when attempting to decode semantic representations from MEG responses to written text

ABSTRACT Several studies have been published which show that it is possible to decode semantic representations directly from brain responses. This has been repeatedly successful when the stimuli used were pictures of objects. However, there is a distinct scarcity of studies decoding responses to orthographic stimuli, particularly those employing time-sensitive imaging methods. We use examples from our own research to highlight the challenges we have faced when attempting to decode semantic representations from MEG responses to written words. We discuss differences in brain responses to pictures and orthographic stimuli to determine the characteristics of the brain responses that allow for successful decoding of semantics. We suspect the limited number of published studies on this topic indicates that these challenges are not unique to our experience. By bringing attention to these issues, we hope to stimulate a new wave of discussion leading to eventual progress.

Behavioral and oscillatory signatures of switch costs in highly proficient bilinguals

Bilinguals with a high proficiency in their first (L1) and second language (L2) often show comparable reaction times when switching from their L1 to L2 and vice-versa (“symmetrical switch costs”). However, the neurophysiological signatures supporting this effect are not well understood. Here, we ran two separate experiments and assessed behavioral and MEG responses in highly proficient Spanish-Basque bilinguals while they overtly name pictures in a mixed-language context. In the behavioral experiment, bilinguals were slower when naming items in switch relative to non-switch trials, and this switch cost was comparable for both languages (symmetrical). The MEG experiment mimicked the behavioral one, with switch trials showing more desynchronization than non-switch trials across languages (symmetric neural cost) in the alpha band (8–13 Hz). Source-localization revealed the engagement of right parietal and premotor areas, which have been linked to language selection and inhibitory control; and of the left anterior temporal lobe (ATL), a cross-linguistic region housing conceptual knowledge that generalizes across languages. Our results suggest that highly proficient bilinguals implement a language-independent mechanism, supported by alpha oscillations, which is involved in cue-based language selection and facilitates conceptually-driven lexical access in the ATL, possibly by inhibiting non-target lexical items or disinhibiting target ones.

Can MEG Source Localization Reveal the Time Course of Processing in the FFA, PPA, and EBA?

fMRI has provided a rich spatial map of category-selective responses across the ventral visual cortex, and EEG/MEG can inform the precise time course of visual processing. But what we really need is both temporal and spatial precision simultaneously. Can MEG source localization reveal the time course of processing in specific functional regions, distinguishing them from nearby regions? Here we addressed that question by collecting both MEG and fMRI responses from the same fifteen participants to the same images of faces, bodies, scenes, and objects. We used fMRI localizers to define the FFA, PPA, and EBA in each participant individually. We then computed source estimates from the MEG data using dynamic statistical parametric mapping, extracting the time courses of estimated MEG source amplitudes to each stimulus category within each fMRI-derived ROI. Source-localized MEG data showed a selective response to bodies peaking at about 160 ms in bilateral EBA that was not present in the FFA or PPA. A highly prominent right-lateralized face response peaking around 130 ms was detected in all three fROIs, while no clear peak response to scenes was present in any fROI. Analyses of source-localized responses across the whole cortex revealed a similar picture, with scene-selective responses spatially blurred across the occipital cortex, right-lateralized face-selective responses spanning much of the ventral surface of the cortex (extending well beyond the fusiform gyrus), and body-selective responses arising later on the lateral surface of both hemispheres in the vicinity of the EBA. These results suggest that right-lateralized face processing peaks on the ventral surface of the brain around 130 ms and body-selective processing on the lateral surface around 160 ms. However, they also indicate that MEG source localization is unable to detect fMRI-expected signals from some regions (PPA) or to precisely localize signals to the spatial grain of fMRI-defined functional regions.

Information redundancy across spatial scales modulates early visual cortical processing

Visual images contain redundant information across spatial scales where low spatial frequency contrast is informative towards the location and likely content of high spatial frequency detail. Previous research suggests that the visual system makes use of those redundancies to facilitate efficient processing. In this framework, a fast, initial analysis of low-spatial frequency (LSF) information guides the slower and later processing of high spatial frequency (HSF) detail. Here, we used multivariate classification as well as time-frequency analysis of MEG responses to the viewing of intact and phase scrambled images of human faces to demonstrate that the availability of redundant LSF information, as found in broadband intact images, correlates with a reduction in HSF representational dominance in both early and higher-level visual areas as well as a reduction of gamma-band power in early visual cortex. Our results indicate that the cross spatial frequency information redundancy that can be found in all natural images might be a driving factor in the efficient integration of fine image details.

A population receptive field model of the magnetoencephalography response

Computational models which predict the neurophysiological response from experimental stimuli have played an important role in human neuroimaging. One type of computational model, the population receptive field (pRF), has been used to describe cortical responses at the millimeter scale using functional magnetic resonance imaging (fMRI) and electrocorticography (ECoG). However, pRF models are not widely used for non-invasive electromagnetic field measurements (EEG/MEG), because individual sensors pool responses originating from several centimeter of cortex, containing neural populations with widely varying spatial tuning. Here, we introduce a forward-modeling approach in which pRFs estimated from fMRI data are used to predict MEG sensor responses. Subjects viewed contrast-reversing bar stimuli sweeping across the visual field in separate fMRI and MEG sessions. Individual subject's pRFs were modeled on the cortical surface at the millimeter scale using the fMRI data. We then predicted cortical time series and projected these predictions to MEG sensors using a biophysical MEG forward model, accounting for the pooling across cortex. We compared the predicted MEG responses to observed visually evoked steady-state responses measured in the MEG session. We found that pRF parameters estimated by fMRI could explain a substantial fraction of the variance in steady-state MEG sensor responses (up to 60% in individual sensors). Control analyses in which we artificially perturbed either pRF size or pRF position reduced MEG prediction accuracy, indicating that MEG data are sensitive to pRF properties derived from fMRI. Our model provides a quantitative approach to link fMRI and MEG measurements, thereby enabling advances in our understanding of spatiotemporal dynamics in human visual field maps.

Predictive Neural Computations Support Spoken Word Recognition: Evidence from MEG and Competitor Priming.

Human listeners achieve quick and effortless speech comprehension through computations of conditional probability using Bayes rule. However, the neural implementation of Bayesian perceptual inference remains unclear. Competitive-selection accounts (e.g., TRACE) propose that word recognition is achieved through direct inhibitory connections between units representing candidate words that share segments (e.g., hygiene and hijack share /haidʒ/). Manipulations that increase lexical uncertainty should increase neural responses associated with word recognition when words cannot be uniquely identified. In contrast, predictive-selection accounts (e.g., Predictive-Coding) propose that spoken word recognition involves comparing heard and predicted speech sounds and using prediction error to update lexical representations. Increased lexical uncertainty in words, such as hygiene and hijack, will increase prediction error and hence neural activity only at later time points when different segments are predicted. We collected MEG data from male and female listeners to test these two Bayesian mechanisms and used a competitor priming manipulation to change the prior probability of specific words. Lexical decision responses showed delayed recognition of target words (hygiene) following presentation of a neighboring prime word (hijack) several minutes earlier. However, this effect was not observed with pseudoword primes (higent) or targets (hijure). Crucially, MEG responses in the STG showed greater neural responses for word-primed words after the point at which they were uniquely identified (after /haidʒ/ in hygiene) but not before while similar changes were again absent for pseudowords. These findings are consistent with accounts of spoken word recognition in which neural computations of prediction error play a central role.SIGNIFICANCE STATEMENT Effective speech perception is critical to daily life and involves computations that combine speech signals with prior knowledge of spoken words (i.e., Bayesian perceptual inference). This study specifies the neural mechanisms that support spoken word recognition by testing two distinct implementations of Bayes perceptual inference. Most established theories propose direct competition between lexical units such that inhibition of irrelevant candidates leads to selection of critical words. Our results instead support predictive-selection theories (e.g., Predictive-Coding): by comparing heard and predicted speech sounds, neural computations of prediction error can help listeners continuously update lexical probabilities, allowing for more rapid word identification.

The visual cortex produces gamma band echo in response to broadband visual flicker.

The aim of this study is to uncover the network dynamics of the human visual cortex by driving it with a broadband random visual flicker. We here applied a broadband flicker (1-720 Hz) while measuring the MEG and then estimated the temporal response function (TRF) between the visual input and the MEG response. This TRF revealed an early response in the 40-60 Hz gamma range as well as in the 8-12 Hz alpha band. While the gamma band response is novel, the latter has been termed the alpha band perceptual echo. The gamma echo preceded the alpha perceptual echo. The dominant frequency of the gamma echo was subject-specific thereby reflecting the individual dynamical properties of the early visual cortex. To understand the neuronal mechanisms generating the gamma echo, we implemented a pyramidal-interneuron gamma (PING) model that produces gamma oscillations in the presence of constant input currents. Applying a broadband input current mimicking the visual stimulation allowed us to estimate TRF between the input current and the population response (akin to the local field potentials). The TRF revealed a gamma echo that was similar to the one we observed in the MEG data. Our results suggest that the visual gamma echo can be explained by the dynamics of the PING model even in the absence of sustained gamma oscillations.

Estimates of cortical column orientation improve MEG source inversion

Determining the anatomical source of brain activity non-invasively measured from EEG or MEG sensors is challenging. In order to simplify the source localization problem, many techniques introduce the assumption that current sources lie on the cortical surface. Another common assumption is that this current flow is orthogonal to the cortical surface, thereby approximating the orientation of cortical columns. However, it is not clear which cortical surface to use to define the current source locations, and normal vectors computed from a single cortical surface may not be the best approximation to the orientation of cortical columns. We compared three different surface location priors and five different approaches for estimating dipole vector orientation, both in simulations and visual and motor evoked MEG responses. We show that models with source locations on the white matter surface and using methods based on establishing correspondences between white matter and pial cortical surfaces dramatically outperform models with source locations on the pial or combined pial/white surfaces and which use methods based on the geometry of a single cortical surface in fitting evoked visual and motor responses. These methods can be easily implemented and adopted in most M/EEG analysis pipelines, with the potential to significantly improve source localization of evoked responses.

Simultaneous EEG and MEG recordings reveal vocal pitch elicited cortical gamma oscillations in young and older adults

The frequency-following response with origin in the auditory brainstem represents the pitch contour of voice and can be recorded with electrodes from the scalp. MEG studies also revealed a cortical contribution to the high gamma oscillations at the fundamental frequency (f0) of a vowel stimulus. Therefore, studying the cortical component of the frequency-following response could provide insights into how pitch information is encoded at the cortical level. Comparing how aging affects the different responses may help to uncover the neural mechanisms underlying speech understanding deficits in older age. We simultaneously recorded EEG and MEG responses to the syllable /ba/. MEG beamformer analysis localized sources in bilateral auditory cortices and the midbrain. Time-frequency analysis showed a faithful representation of the pitch contour between 106 Hz and 138 Hz in the cortical activity. A cross-correlation revealed a latency of 20 ms. Furthermore, stimulus onsets elicited cortical 40-Hz responses. Both the 40-Hz and the f0 response amplitudes increased in older age and were larger in the right hemisphere. The effects of aging and laterality of the f0 response were evident in the MEG only, suggesting that both effects were characteristics of the cortical response. After comparing f0 and N1 responses in EEG and MEG, we estimated that approximately one-third of the scalp-recorded f0 response could be cortical in origin. We attributed the significance of the cortical f0 response to the precise timing of cortical neurons that serve as a time-sensitive code for pitch.

A model-based approach to link MEG responses to neuronal synchrony in visual cortex

A model-based approach to link MEG responses to neuronal synchrony in visual cortex

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.

Automatic access to verb continuations on the lexical and categorical levels: evidence from MEG

ABSTRACTLittle is known about how readers represent probabilistic information about verb continuations (VCs), or to what degree automatic tracking of these probability distributions constitutes anticipatory processing. Research shows that subcategorization frame entropy, a measure indexing uncertainty about the syntactic constituent following a verb, affects neural responses during verb recognition. This suggests readers have mental representations of VCs. However, the granularity of these representations remains unclear. Using MEG, we investigated how precisely, and to what purpose, the brain represents VCs in single-word processing. We found that, compared to SCF entropy, entropy over a granularized set of continuations (created by subdividing the prepositional phrase frame) better predicted behavioural and MEG responses, suggesting readers access continuations more fine-grained than grammatical category. Results also revealed a spatiotemporally separate sensitivity to the distribution of lexemes following verbs. Overall, our findings suggest that readers represent VCs on multiple levels, and likely engage in anticipatory processing automatically.

Auditory cortex responses to interaural time differences in the envelope of low-frequency sound, recorded with MEG in young and older listeners

Interaural time and intensity differences (ITD and IID) are important cues in binaural hearing and allow for sound localization, improving speech understanding in noise and reverberation, and integrating sound sources in the auditory scene. Whereas previous research showed that the upper-frequency limit for ITD detection in the fine structure of sound declines in aging, the processing of envelope ITD in low-frequency amplitude modulated (AM) sound and the related brain responses are less understood. This study investigated the cortical processing of envelope ITD and compared the results with previous findings about the fine-structure ITD. In two experiments, participants listened to 40-Hz AM tones containing sudden changes in the envelope ITD. Multiple MEG responses were analyzed, including the auditory evoked N1 responses, elicited both by sound onsets and ITD changes, and 40-Hz responses, elicited by the AM. The first experiment with healthy young adults revealed a substantial decline in the magnitudes of the ITD change N1 response, and the 40-Hz phase resets at higher carrier frequencies, suggesting a similar frequency characteristic as observed for fine structure ITD. The amplitude of the 40-Hz ASSR declined only gradually with increasing carrier frequency, and it was excluded as a confounding factor in the decline in the ITD response. Larger responses to outward ITD changes than inward changes, here first reported for envelope ITD, were another characteristics that were similar to fine-structure ITD. A second experiment with groups of young and older listeners examined the effects of aging and concurrent noise on the cortical envelope ITD responses. One important research question was, whether binaural cues are accessible in noise. Behavioural tests showed an age-related hearing loss in the older group and decreased performance in envelope ITD detection and speech-in-noise (SIN) understanding. Binaural hearing and SIN performance were correlated with one other, but not with hearing loss. The frequency limit for envelope ITD was reduced in older listeners similarly as previously found for fine structure ITD, and older listeners were more susceptible to concurrent multi-talker noise. The similarities between responses to envelope ITD and to fine structure ITD suggest that a common cortical code exists for the envelope and fine structure ITD. The dependency on the carrier frequency suggests that envelope ITD processing at the subcortical level requires stimulus phase locking, which might be reduced in aging.

The effect of task on categorization behavior and its relationship to brain and deep neural networks

A key objective in neuroscience is to understand how brains produce behavior. For the visual processing of objects, one approach is to relate categorization behavior to object representations at different cortical processing stages. A popular method to assess behavior is the object arrangement task, in which participants arrange objects in a 2D "arena" based on their relative similarity. While this method is efficient in producing representational dissimilarity matrices (RDMs) and is well-suited for uncovering low-dimensional representations or clearly defined clusters, it is prone to contextual biases and may be suboptimal for higher-dimensional or more continuous representations. Here we investigate the triplet task as an alternative approach for studying behavioral similarity. In this task, on each trial a participant has to choose an "odd-one out" from a set of three stimuli, yielding three binary similarity measures per response. For small to intermediate RDMs, this approach is efficient and cheap when carried out online in a distributed fashion, quickly yielding responses from hundreds of participants, while minimizing contextual effects. We compared RDMs obtained from both tasks using a set of 48 representative objects and tested their correspondence to deep neural networks (DNNs) that exhibit human-like categorization performance. While the RDMs between both tasks were highly correlated, the triplet RDM correlated much more strongly with DNN layers than the arrangement task. Using a commonly studied, more structured set of 92 objects, our results revealed comparable relationship to DNNs. However, the triplet task outperformed the arrangement task in explaining responses in human IT and later MEG responses, while earliest MEG responses were dominated by the arrangement task. Together, these results reveal the importance of the behavioral metric in teasing apart the relationship between brain, DNNs, and behavior. Further, our method provides a novel, efficient approach for obtaining behavioral measures of similarity. Meeting abstract presented at VSS 2018

Consistency and similarity of MEG- and fMRI-signal time courses during movie viewing

Movie viewing allows human perception and cognition to be studied in complex, real-life-like situations in a brain-imaging laboratory. Previous studies with functional magnetic resonance imaging (fMRI) and with magneto- and electroencephalography (MEG and EEG) have demonstrated consistent temporal dynamics of brain activity across movie viewers. However, little is known about the similarities and differences of fMRI and MEG or EEG dynamics during such naturalistic situations.We thus compared MEG and fMRI responses to the same 15-min black-and-white movie in the same eight subjects who watched the movie twice during both MEG and fMRI recordings. We analyzed intra- and intersubject voxel-wise correlations within each imaging modality as well as the correlation of the MEG envelopes and fMRI signals. The fMRI signals showed voxel-wise within- and between-subjects correlations up to r = 0.66 and r = 0.37, respectively, whereas these correlations were clearly weaker for the envelopes of band-pass filtered (7 frequency bands below 100 Hz) MEG signals (within-subjects correlation r < 0.14 and between-subjects r < 0.05). Direct MEG–fMRI voxel-wise correlations were unreliable. Notably, applying a spatial-filtering approach to the MEG data uncovered consistent canonical variates that showed considerably stronger (up to r = 0.25) between-subjects correlations than the univariate voxel-wise analysis. Furthermore, the envelopes of the time courses of these variates up to about 10 Hz showed association with fMRI signals in a general linear model. Similarities between envelopes of MEG canonical variates and fMRI voxel time-courses were seen mostly in occipital, but also in temporal and frontal brain regions, whereas intra- and intersubject correlations for MEG and fMRI separately were strongest only in the occipital areas.In contrast to the conventional univariate analysis, the spatial-filtering approach was able to uncover associations between the MEG envelopes and fMRI time courses, shedding light on the similarities of hemodynamic and electromagnetic brain activities during movie viewing.

Decoding the orientation of contrast edges from MEG evoked and induced responses

Visual gamma oscillations have been proposed to subserve perceptual binding, but their strong modulation by diverse stimulus features confounds interpretations of their precise functional role. Overcoming this challenge necessitates a comprehensive account of the relationship between gamma responses and stimulus features. Here we used multivariate pattern analyses on human MEG data to characterize the relationships between gamma responses and one basic stimulus feature, the orientation of contrast edges. Our findings confirmed we could decode orientation information from induced responses in two dominant frequency bands at 24–32 Hz and 50–58 Hz. Decoding was higher for cardinal than oblique orientations, with similar results also obtained for evoked MEG responses. In contrast to multivariate analyses, orientation information was mostly absent in univariate signals: evoked and induced responses in early visual cortex were similar in all orientations, with only exception an inverse oblique effect observed in induced responses, such that cardinal orientations produced weaker oscillatory signals than oblique orientations. Taken together, our results showed multivariate methods are well suited for the analysis of gamma oscillations, with multivariate patterns robustly encoding orientation information and predominantly discriminating cardinal from oblique stimuli.