MNE software for processing MEG and EEG data

Multiscale Canonical Coherence for Functional Corticomuscular Coupling Analysis

Functional corticomuscular coupling (FCMC) probes multi-level information communication in the sensorimotor system. The canonical Coherence (caCOH) method has been leveraged to measure the FCMC between two multivariate signals within the single scale. In this paper, we propose the concept of multiscale canonical Coherence (MS-caCOH) to disentangle complex multi-layer information and extract coupling features in multivariate spaces from multiple scales. Then, we verified the reliability and effectiveness of MS-caCOH on two types of data sets, i.e., a synthetic multivariate data set and a real-world multivariate data set. Our simulation results showed that compared with caCOH, MS-caCOH enhanced coupling detection and achieved lower pattern recovery errors at multiple frequency scales. Further analysis on experimental data demonstrated that the proposed MS-caCOH method could also capture detailed multiscale spatial-frequency characteristics. This study leverages the multiscale analysis framework and multivariate method to give a new insight into corticomuscular coupling analysis.

Distinct patterns of spatial attentional modulation of steady-state visual evoked magnetic fields (SSVEFs) in subdivisions of the human early visual cortex

In recent years, steady-state visual evoked potentials (SSVEPs) became an increasingly valuable tool to investigate neural dynamics of competitive attentional interactions and brain-computer interfaces. This is due to their good signal-to-noise ratio, allowing for single-trial analysis, and their ongoing oscillating nature that enables to analyze temporal dynamics of facilitation and suppression. Given the popularity of SSVEPs, it is surprising that only a few studies looked at the cortical sources of these responses. This is in particular the case when searching for studies that assessed the cortical sources of attentional SSVEP amplitude modulations. To address this issue, we used a typical spatial attention task and recorded neuromagnetic fields (MEG) while presenting frequency-tagged stimuli in the left and right visual fields, respectively. Importantly, we controlled for attentional deployment in a baseline period before the shifting cue. Subjects either attended to a central fixation cross or to two peripheral stimuli simultaneously. Results clearly showed that signal sources and attention effects were restricted to the early visual cortex: V1, V2, hMT+, precuneus, occipital-parietal, and inferior-temporal cortex. When subjects attended to central fixation first, shifting attention to one of the peripheral stimuli resulted in a significant activation increase for the to-be-attended stimulus with no activation decrease for the to-be-ignored stimulus in hMT+ and inferio-temporal cortex, but significant SSVEF decreases from V1 to occipito-parietal cortex. When attention was first deployed to both rings, shifting attention away from one ring basically resulted in a significant activation decrease in all areas for the then-to-be-ignored stimulus.

Limiting parameter range for cortical-spherical mapping improves activated domain estimation for attention modulated auditory response

BACKGROUND

Attention is one of the factors involved in selecting input information for the brain. We applied a method for estimating domains with clear boundaries using magnetoencephalography (the domain estimation method) for auditory-evoked responses (N100m) to evaluate the effects of attention in milliseconds. However, because the surface around the auditory cortex is folded in a complicated manner, it is unknown whether the activity in the auditory cortex can be estimated.

NEW METHOD

The parameter range to express current sources was set to include the auditory cortex. Their search region was expressed as a direct product of the parameter ranges used in the adaptive diagonal curves.

RESULTS

Without a limitation of the range, activity was estimated in regions other than the auditory cortex in all cases. However, with the limitation of the range, the activity was estimated in the primary or higher auditory cortex. Further analysis of the limitation of the range showed that the domains activated during attention included the regions activated during no attention for the participants whose amplitudes of N100m were higher during attention.

COMPARISON WITH EXISTING METHOD

We proposed a method for effectively limiting the search region to evaluate the extent of the activated domain in regions with complex folded structures.

CONCLUSION

To evaluate the extent of activated domains in regions with complex folded structures, it is necessary to limit the parameter search range. The area of the activated domains in the auditory cortex may increase by attention on the millisecond timescale.

The control patterns of affective processing and cognitive reappraisal: insights from brain controllability analysis

Perceiving and modulating emotions is vital for cognitive function and is often impaired in neuropsychiatric conditions. Current tools for evaluating emotional dysregulation suffer from subjectivity and lack of precision, especially when it comes to understanding emotion from a regulatory or control-based perspective. To address these limitations, this study leverages an advanced methodology known as functional brain controllability analysis. We simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data from 17 healthy subjects engaged in emotion processing and regulation tasks. We then employed a novel EEG/fMRI integration technique to reconstruct cortical activity in a high spatiotemporal resolution manner. Subsequently, we conducted functional brain controllability analysis to explore the neural network control patterns underlying different emotion conditions. Our findings demonstrated that the dorsolateral and ventrolateral prefrontal cortex exhibited increased controllability during the processing and regulation of negative emotions compared to processing of neutral emotion. Besides, the anterior cingulate cortex was notably more active in managing negative emotion than in either controlling neutral emotion or regulating negative emotion. Finally, the posterior parietal cortex emerged as a central network controller for the regulation of negative emotion. This study offers valuable insights into the cortical control mechanisms that support emotion perception and regulation.

Strengthening of alpha synchronization is a neural correlate of cognitive transfer

Cognitive training can lead to improvements in both task-specific strategies and general capacities, such as visuo-spatial working memory (VSWM). The latter emerge slowly and linearly throughout training, in contrast to strategy where changes typically occur within the first days of training. Changes in strategy and capacity have not been separated in prior neuroimaging studies. Here, we used a within-participants design with dense temporal sampling to capture the time dynamics of neural mechanisms associated with change in capacity. In four participants, neural activity was recorded with magnetoencephalography on seven occasions over two months of visuo-spatial working memory training. During scanning, the participants performed a trained visuo-spatial working memory task, a transfer task, and a control task. First, we extracted an individual visuo-spatial working memory-load-dependent synchronization network for each participant. Next, we identified linear changes over time in the network, congruent with the temporal dynamics of capacity change. Three out of four participants showed a gradual strengthening of alpha synchronization. Strengthening of the same connections was also found in the transfer task but not in the control task. This suggests that cognitive transfer occurs through slow, gradual strengthening of alpha synchronization between cortical regions that are vital for both the trained task and the transfer task.

Measuring neuronal avalanches to inform brain-computer interfaces

Large-scale interactions among multiple brain regions manifest as bursts of activations called neuronal avalanches, which reconfigure according to the task at hand and, hence, might constitute natural candidates to design brain-computer interfaces (BCIs). To test this hypothesis, we used source-reconstructed magneto/electroencephalography during resting state and a motor imagery task performed within a BCI protocol. To track the probability that an avalanche would spread across any two regions, we built an avalanche transition matrix (ATM) and demonstrated that the edges whose transition probabilities significantly differed between conditions hinged selectively on premotor regions in all subjects. Furthermore, we showed that the topology of the ATMs allows task-decoding above the current gold standard. Hence, our results suggest that neuronal avalanches might capture interpretable differences between tasks that can be used to inform brain-computer interfaces.

M-FANet: Multi-Feature Attention Convolutional Neural Network for Motor Imagery Decoding

Motor imagery (MI) decoding methods are pivotal in advancing rehabilitation and motor control research. Effective extraction of spectral-spatial-temporal features is crucial for MI decoding from limited and low signal-to-noise ratio electroencephalogram (EEG) signal samples based on brain-computer interface (BCI). In this paper, we propose a lightweight Multi-Feature Attention Neural Network (M-FANet) for feature extraction and selection of multi-feature data. M-FANet employs several unique attention modules to eliminate redundant information in the frequency domain, enhance local spatial feature extraction and calibrate feature maps. We introduce a training method called Regularized Dropout (R-Drop) to address training-inference inconsistency caused by dropout and improve the model's generalization capability. We conduct extensive experiments on the BCI Competition IV 2a (BCIC-IV-2a) dataset and the 2019 World robot conference contest-BCI Robot Contest MI (WBCIC-MI) dataset. M-FANet achieves superior performance compared to state-of-the-art MI decoding methods, with 79.28% 4-class classification accuracy (kappa: 0.7259) on the BCIC-IV-2a dataset and 77.86% 3-class classification accuracy (kappa: 0.6650) on the WBCIC-MI dataset. The application of multi-feature attention modules and R-Drop in our lightweight model significantly enhances its performance, validated through comprehensive ablation experiments and visualizations.

The effect of voice familiarity on attention to speech in a cocktail party scenario

Selective attention to one speaker in multi-talker environments can be affected by the acoustic and semantic properties of speech. One highly ecological feature of speech that has the potential to assist in selective attention is voice familiarity. Here, we tested how voice familiarity interacts with selective attention by measuring the neural speech-tracking response to both target and non-target speech in a dichotic listening "Cocktail Party" paradigm. We measured Magnetoencephalography from n = 33 participants, presented with concurrent narratives in two different voices, and instructed to pay attention to one ear ("target") and ignore the other ("non-target"). Participants were familiarized with one of the voices during the week prior to the experiment, rendering this voice familiar to them. Using multivariate speech-tracking analysis we estimated the neural responses to both stimuli and replicate their well-established modulation by selective attention. Importantly, speech-tracking was also affected by voice familiarity, showing enhanced response for target speech and reduced response for non-target speech in the contra-lateral hemisphere, when these were in a familiar vs. an unfamiliar voice. These findings offer valuable insight into how voice familiarity, and by extension, auditory-semantics, interact with goal-driven attention, and facilitate perceptual organization and speech processing in noisy environments.

Functional and anatomical connectivity predict brain stimulation's mnemonic effects

Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation's behavioral and physiological effects depend on the stimulation target's anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered the stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain's memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation's behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.

Neural Representations of Non-native Speech Reflect Proficiency and Interference from Native Language Knowledge

Learning to process speech in a foreign language involves learning new representations for mapping the auditory signal to linguistic structure. Behavioral experiments suggest that even listeners that are highly proficient in a non-native language experience interference from representations of their native language. However, much of the evidence for such interference comes from tasks that may inadvertently increase the salience of native language competitors. Here we tested for neural evidence of proficiency and native language interference in a naturalistic story listening task. We studied electroencephalography responses of 39 native speakers of Dutch (14 male) to an English short story, spoken by a native speaker of either American English or Dutch. We modeled brain responses with multivariate temporal response functions, using acoustic and language models. We found evidence for activation of Dutch language statistics when listening to English, but only when it was spoken with a Dutch accent. This suggests that a naturalistic, monolingual setting decreases the interference from native language representations, whereas an accent in the listener's own native language may increase native language interference, by increasing the salience of the native language and activating native language phonetic and lexical representations. Brain responses suggest that such interference stems from words from the native language competing with the foreign language in a single word recognition system, rather than being activated in a parallel lexicon. We further found that secondary acoustic representations of speech (after 200 ms latency) decreased with increasing proficiency. This may reflect improved acoustic-phonetic models in more proficient listeners.Significance Statement Behavioral experiments suggest that native language knowledge interferes with foreign language listening, but such effects may be sensitive to task manipulations, as tasks that increase metalinguistic awareness may also increase native language interference. This highlights the need for studying non-native speech processing using naturalistic tasks. We measured neural responses unobtrusively while participants listened for comprehension and characterized the influence of proficiency at multiple levels of representation. We found that salience of the native language, as manipulated through speaker accent, affected activation of native language representations: significant evidence for activation of native language (Dutch) categories was only obtained when the speaker had a Dutch accent, whereas no significant interference was found to a speaker with a native (American) accent.

Dual-Alpha: a large EEG study for dual-frequency SSVEP brain-computer interface

BACKGROUND

The domain of brain-computer interface (BCI) technology has experienced significant expansion in recent years. However, the field continues to face a pivotal challenge due to the dearth of high-quality datasets. This lack of robust datasets serves as a bottleneck, constraining the progression of algorithmic innovations and, by extension, the maturation of the BCI field.

FINDINGS

This study details the acquisition and compilation of electroencephalogram data across 3 distinct dual-frequency steady-state visual evoked potential (SSVEP) paradigms, encompassing over 100 participants. Each experimental condition featured 40 individual targets with 5 repetitions per target, culminating in a comprehensive dataset consisting of 21,000 trials of dual-frequency SSVEP recordings. We performed an exhaustive validation of the dataset through signal-to-noise ratio analyses and task-related component analysis, thereby substantiating its reliability and effectiveness for classification tasks.

CONCLUSIONS

The extensive dataset presented is set to be a catalyst for the accelerated development of BCI technologies. Its significance extends beyond the BCI sphere and holds considerable promise for propelling research in psychology and neuroscience. The dataset is particularly invaluable for discerning the complex dynamics of binocular visual resource distribution.

MetaBCI: An open-source platform for brain-computer interfaces

BACKGROUND

Recently, brain-computer interfaces (BCIs) have attracted worldwide attention for their great potential in clinical and real-life applications. To implement a complete BCI system, one must set up several links to translate the brain intent into computer commands. However, there is not an open-source software platform that can cover all links of the BCI chain.

METHOD

This study developed a one-stop open-source BCI software, namely MetaBCI, to facilitate the construction of a BCI system. MetaBCI is written in Python, and has the functions of stimulus presentation (Brainstim), data loading and processing (Brainda), and online information flow (Brainflow). This paper introduces the detailed information of MetaBCI and presents four typical application cases.

RESULTS

The results showed that MetaBCI was an extensible and feature-rich software platform for BCI research and application, which could effectively encode, decode, and feedback brain activities.

CONCLUSIONS

MetaBCI can greatly lower the BCI's technical threshold for BCI beginners and can save time and cost to build up a practical BCI system. The source code is available at https://github.com/TBC-TJU/MetaBCI, expecting new contributions from the BCI community.

Functional connectivity of sensorimotor network is enhanced in spastic diplegic cerebral palsy: A multimodal study using fMRI and MEG

OBJECTIVE

To assess the effects to functional connectivity (FC) caused by lesions related to spastic diplegic cerebral palsy (CP) in children and adolescents using multiple imaging modalities.

METHODS

We used resting state magnetoencephalography (MEG) envelope signals in alpha, beta and gamma ranges and resting state functional magnetic resonance imaging (fMRI) signals to quantify FC between selected sensorimotor regions of interest (ROIs) in 11 adolescents with spastic diplegic cerebral palsy and 24 typically developing controls. Motor performance of the hands was quantified with gross motor, fine motor and kinesthesia tests.

RESULTS

In fMRI, participants with CP showed enhanced FC within posterior parietal regions; in MEG, they showed enhanced interhemispheric FC between sensorimotor regions and posterior parietal regions both in alpha and lower beta bands. There was a correlation between the kinesthesia score and fronto-parietal connectivity in the control population.

CONCLUSIONS

CP is associated with enhanced FC in sensorimotor network. This difference is not correlated with hand coordination performance. The effect of the lesion is likely not fully captured by temporal correlation of ROI signals.

SIGNIFICANCE

Brain lesions can show as increased temporal correlation of activity between remote brain areas. We suggest this effect is likely separate from typical physiological correlates of functional connectivity.

Parkinsonian rest tremor can be distinguished from voluntary hand movements based on subthalamic and cortical activity

OBJECTIVE

To distinguish Parkinsonian rest tremor and different voluntary hand movements by analyzing brain activity.

METHODS

We re-analyzed magnetoencephalography and local field potential recordings from the subthalamic nucleus of six patients with Parkinson's disease. Data were obtained after withdrawal from dopaminergic medication (Med Off) and after administration of levodopa (Med On). Using gradient-boosted tree learning, we classified epochs as tremor, fist-clenching, forearm extension or tremor-free rest.

RESULTS

Subthalamic activity alone was insufficient for distinguishing the four different motor states (balanced accuracy mean: 38%, std: 7%). The combination of cortical and subthalamic features, in contrast, allowed for a much more accurate classification (balanced accuracy mean: 75%, std: 17%). Adding a single cortical area improved balanced accuracy by 17% on average, as compared to classification based on subthalamic activity alone. In most patients, the most informative cortical areas were sensorimotor cortical regions. Decoding performance was similar in Med On and Med Off.

CONCLUSIONS

Electrophysiological recordings allow for distinguishing several motor states, provided that cortical signals are monitored in addition to subthalamic activity.

SIGNIFICANCE

By combining cortical recordings, subcortical recordings and machine learning, adaptive deep brain stimulation systems might be able to detect tremor specifically and to respond adequately to several motor states.

Functional and structural connectivity correlates of semantic verbal fluency deficits in first-episode psychosis

INTRODUCTION

Semantic verbal fluency (SVF) impairments are debilitating and present early in the course of psychotic illness. Deficits within frontal, parietal, and temporal brain regions contribute to this deficit, as long-range communication across this functionally integrated network is critical to SVF. This study sought to isolate disruptions in functional and structural connectivity contributing to SVF deficits during first-episode psychosis in the schizophrenia spectrum (FESz).

METHODS

Thirty-three FESz and 34 matched healthy controls (HC) completed the Animal Naming Task to assess SVF. Magnetoencephalography was recorded during an analogous covert SVF task, and phase-locking value (PLV) used to measure functional connectivity between inferior frontal and temporoparietal structures bilaterally. Diffusion imaging was collected to measure fractional anisotropy (FA) of the arcuate fasciculus, the major tract connecting frontal and temporoparietal language areas.

RESULTS

SVF scores were lower among FESz compared to HC. While PLV and FA did not differ between groups overall, FESz exhibited an absence of the left-lateralized nature of both measures observed in HC. Among FESz, larger right-hemisphere PLV was associated with worse SVF performance (ρ = -0.51) and longer DUP (ρ = -0.50).

DISCUSSION

In addition to worse SVF, FESz exhibited diminished leftward asymmetry of structural and functional connectivity in fronto-temporoparietal SVF network. The relationship between theta-band hyperconnectivity and poorer performance suggests a disorganized executive network and may reflect dysfunction of frontal cognitive control centers. These findings illustrate an aberrant pattern across the distributed SVF network at disease onset and merit further investigation into development of asymmetrical hemispheric connectivity and its failure among high-risk populations.

Beta- and gamma-band cortico-cortical interactions support naturalistic reading of continuous text

Large-scale integration of information across cortical structures, building on neural connectivity, has been proposed to be a key element in supporting human cognitive processing. In electrophysiological neuroimaging studies of reading, quantification of neural interactions has been limited to the level of isolated words or sentences due to artefacts induced by eye movements. Here, we combined magnetoencephalography recording with advanced artefact rejection tools to investigate both cortico-cortical coherence and directed neural interactions during naturalistic reading of full-page texts. Our results show that reading versus visual scanning of text was associated with wide-spread increases of cortico-cortical coherence in the beta and gamma bands. We further show that the reading task was linked to increased directed neural interactions compared to the scanning task across a sparse set of connections within a wide range of frequencies. Together, the results demonstrate that neural connectivity flexibly builds on different frequency bands to support continuous natural reading.

Consolidation improves the learning of new meanings for known words but not necessarily their integration into semantic memory

Consolidation is essential to the integration of novel words into the mental lexicon; however, its role in learning new meanings for known words remains unclear. This old-form-new-meaning learning is very common, as when one learns that "skate" is also a type of fish in addition to its familiar roller- or ice-skating meaning. To address consolidation effects for new meanings, we compared the behavioral and ERP measures on new and original meanings tested 24 hours after learning with words tested immediately after learning. Semantic judgments of both new and original meanings benefitted from the study-test interval. However, N400 amplitudes on studied words-indicators of meaning access from semantic memory-were unaffected by learning or consolidation. These results suggest that while sleep benefits memory for new meanings, the new meanings do not become integrated into the mental lexicon within that period. Instead, episodic retrieval remains functional in accessing new meanings even after 24 hours.

Strength of predicted information content in the brain biases decision behavior

Prediction-for-perception theories suggest that the brain predicts incoming stimuli to facilitate their categorization.1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17 However, it remains unknown what the information contents of these predictions are, which hinders mechanistic explanations. This is because typical approaches cast predictions as an underconstrained contrast between two categories18,19,20,21,22,23,24-e.g., faces versus cars, which could lead to predictions of features specific to faces or cars, or features from both categories. Here, to pinpoint the information contents of predictions and thus their mechanistic processing in the brain, we identified the features that enable two different categorical perceptions of the same stimuli. We then trained multivariate classifiers to discern, from dynamic MEG brain responses, the features tied to each perception. With an auditory cueing design, we reveal where, when, and how the brain reactivates visual category features (versus the typical category contrast) before the stimulus is shown. We demonstrate that the predictions of category features have a more direct influence (bias) on subsequent decision behavior in participants than the typical category contrast. Specifically, these predictions are more precisely localized in the brain (lateralized), are more specifically driven by the auditory cues, and their reactivation strength before a stimulus presentation exerts a greater bias on how the individual participant later categorizes this stimulus. By characterizing the specific information contents that the brain predicts and then processes, our findings provide new insights into the brain's mechanisms of prediction for perception.

Using spectral and temporal filters with EEG signal to predict the temporal lobe epilepsy outcome after antiseizure medication via machine learning

Epilepsy is a neurological disorder in which the brain is transiently altered. Predicting outcomes in epilepsy is essential for providing feedback that can foster improved outcomes in the future. This study aimed to investigate whether applying spectral and temporal filters to resting-state electroencephalography (EEG) signals could improve the prediction of outcomes for patients taking antiseizure medication to treat temporal lobe epilepsy (TLE). We collected EEG data from a total of 46 patients (divided into a seizure-free group (SF, n = 22) and a non-seizure-free group (NSF, n = 24)) with TLE and retrospectively reviewed their clinical data. We segmented spectral and temporal ranges with various time-domain features (Hjorth parameters, statistical parameters, energy, zero-crossing rate, inter-channel correlation, inter-channel phase locking value and spectral information derived from Fourier transform, Stockwell transform, and wavelet transform) and compared their performance by applying an optimal frequency strategy, an optimal duration strategy, and a combination strategy. For all time-domain features, the optimal frequency and time combination strategy showed the highest performance in distinguishing SF patients from NSF patients (area under the curve (AUC) = 0.790 ± 0.159). Furthermore, optimal performance was achieved by utilizing a feature vector derived from statistical parameters within the 39- to 41-Hz frequency band with a window length of 210 s, as evidenced by an AUC of 0.748. By identifying the optimal parameters, we improved the performance of the prediction model. These parameters can serve as standard parameters for predicting outcomes based on resting-state EEG signals.

Comparison of online and offline applications of dual-site transcranial alternating current stimulation (tACS) over the pre-supplementary motor area (preSMA) and right inferior frontal gyrus (rIFG) for improving response inhibition

The efficacy of transcranial alternating current stimulation (tACS) is thought to be brain state-dependent, such that tACS during task performance would be hypothesised to offer greater potential for improving performance compared to tACS at rest. However, to date, no empirical study has tested this postulation. The current study compared the effects of dual-site beta tACS applied during a stop signal task (online) to the effects of the same tACS protocol applied prior to the task (offline) and a sham control stimulation in 53 young, healthy adults (32 female; 18-35 yrs). The right inferior frontal gyrus (rIFG) and centre (midline) of the pre-supplementary motor area (preSMA), which are thought to play critical roles in action cancellation, were simultaneously stimulated, sending phase-synchronised stimulation for 15 min with the aim of increasing functional connectivity. The offline group showed significant within-group improvement in response inhibition without showing overt task-related changes in functional connectivity measured with EEG connectivity analysis, suggesting offline tACS is efficacious in inducing behavioural changes potentially via a post-stimulation early plasticity mechanism. In contrast, neither the online nor sham group showed significant improvements in response inhibition. However, EEG connectivity analysis revealed significantly increased task-related functional connectivity following online stimulation and a medium effect size observed in correlation analyses suggested that an increase in functional connectivity in the beta band at rest was potentially associated with an improvement in response inhibition. Overall, the results indicate that both online and offline dual-site beta tACS can be beneficial in improving inhibitory control via distinct underlying mechanisms.

L. M, Narawa Y, Srinath R, Kanitkar M. Improved sleep, cognitive processing and enhanced learning and memory task accuracy with Yoga nidra practice in novices

Complementary and Alternative medicine is known to have health benefits. Yoga nidra practice is an easy-to-do practice and has shown beneficial effects on stress reduction and is found to improve sleep in insomnia patients. Effect of yoga nidra practice on subjective sleep is known but its effect on sleep and cognition objectively is not documented. The aim of the study was to study the effect of yoga nidra practice on cognition and sleep using objective parameters. 41 participants were enrolled, and baseline sleep diary (SD) collected. Participants volunteered for overnight polysomnography (PSG) and cognition testing battery (CTB) comprising of Motor praxis test, emotion recognition task (ERT), digital symbol substitution task, visual object learning task (VOLT), abstract matching (AIM), line orientation task, matrix reasoning task, fractal-2-back test (NBACK), psychomotor vigilance task and balloon analog risk task. Baseline CTB and after one and two weeks of practice was compared. Power spectra density for EEG at central, frontal, and occipital locations during CTB was compared. Repeat SD and PSG after four weeks of practice were done. After yoga nidra practice, improved reaction times for all cognition tasks were seen. Post intervention compared to baseline (95%CI; p-value, effect size) showed a significant improvement in sleep efficiency of +3.62% (0.3, 5.15; p = 0.03, r = 0.42), -20min (-35.78, -5.02; p = 0.003, d = 0.84) for wake after sleep onset and +4.19 μV2 (0.5, 9.5; p = 0.04, r = 0.43) in delta during deep sleep. Accuracy increased in VOLT (95% CI: 0.08, 0.17; p = 0.002, d = 0.79), AIM (95% CI: 0.03, 0.12; p = 0.02, d = 0.61) and NBACK (95% CI: 0.02, 0.13; p = 0.04, d = 0.56); ERT accuracy increased for happy, fear and anger (95% CI: 0.07, 0.24; p = 0.004, d = 0.75) but reduced for neutral stimuli (95% CI: -0.31, -0.12; p = 0.04, r = 0.33) after yoga nidra practice. Yoga Nidra practice improved cognitive processing and night-time sleep.

Your place or mine? The neural dynamics of personally familiar scene recognition suggests category independent familiarity encoding

Recognizing a stimulus as familiar is an important capacity in our everyday life. Recent investigation of visual processes has led to important insights into the nature of the neural representations of familiarity for human faces. Still, little is known about how familiarity affects the neural dynamics of non-face stimulus processing. Here we report the results of an EEG study, examining the representational dynamics of personally familiar scenes. Participants viewed highly variable images of their own apartments and unfamiliar ones, as well as personally familiar and unfamiliar faces. Multivariate pattern analyses were used to examine the time course of differential processing of familiar and unfamiliar stimuli. Time-resolved classification revealed that familiarity is decodable from the EEG data similarly for scenes and faces. The temporal dynamics showed delayed onsets and peaks for scenes as compared to faces. Familiarity information, starting at 200 ms, generalized across stimulus categories and led to a robust familiarity effect. In addition, familiarity enhanced category representations in early (250-300 ms) and later (>400 ms) processing stages. Our results extend previous face familiarity results to another stimulus category and suggest that familiarity as a construct can be understood as a general, stimulus-independent processing step during recognition.

Complexity and 1/f slope jointly reflect brain states

Characterization of brain states is essential for understanding its functioning in the absence of external stimuli. Brain states differ on their balance between excitation and inhibition, and on the diversity of their activity patterns. These can be respectively indexed by 1/f slope and Lempel-Ziv complexity (LZc). However, whether and how these two brain state properties relate remain elusive. Here we analyzed the relation between 1/f slope and LZc with two in-silico approaches and in both rat EEG and monkey ECoG data. We contrasted resting state with propofol anesthesia, which directly modulates the excitation-inhibition balance. We found convergent results among simulated and empirical data, showing a strong, inverse and non trivial monotonic relation between 1/f slope and complexity, consistent at both ECoG and EEG scales. We hypothesize that differentially entropic regimes could underlie the link between the excitation-inhibition balance and the vastness of the repertoire of brain systems.

Adaptive biasing of action-selective cortical build-up activity by stimulus history

Decisions under uncertainty are often biased by the history of preceding sensory input, behavioral choices, or received outcomes. Behavioral studies of perceptual decisions suggest that such history-dependent biases affect the accumulation of evidence and can be adapted to the correlation structure of the sensory environment. Here, we systematically varied this correlation structure while human participants performed a canonical perceptual choice task. We tracked the trial-by-trial variations of history biases via behavioral modeling and of a neural signature of decision formation via magnetoencephalography (MEG). The history bias was flexibly adapted to the environment and exerted a selective effect on the build-up (not baseline level) of action-selective motor cortical activity during decision formation. This effect added to the impact of the current stimulus. We conclude that the build-up of action plans in human motor cortical circuits is shaped by dynamic prior expectations that result from an adaptive interaction with the environment.

Bursting with Potential: How Sensorimotor Beta Bursts Develop from Infancy to Adulthood

Beta activity is thought to play a critical role in sensorimotor processes. However, little is known about how activity in this frequency band develops. Here, we investigated the developmental trajectory of sensorimotor beta activity from infancy to adulthood. We recorded EEG from 9-month-old, 12-month-old, and adult humans (male and female) while they observed and executed grasping movements. We analyzed "beta burst" activity using a novel method that combines time-frequency decomposition and principal component analysis. We then examined the changes in burst rate and waveform motifs along the selected principal components. Our results reveal systematic changes in beta activity during action execution across development. We found a decrease in beta burst rate during movement execution in all age groups, with the greatest decrease observed in adults. Additionally, we identified three principal components that defined waveform motifs that systematically changed throughout the trial. We found that bursts with waveform shapes closer to the median waveform were not rate-modulated, whereas those with waveform shapes further from the median were differentially rate-modulated. Interestingly, the decrease in the rate of certain burst motifs occurred earlier during movement and was more lateralized in adults than in infants, suggesting that the rate modulation of specific types of beta bursts becomes increasingly refined with age.SIGNIFICANCE STATEMENT We demonstrate that, like in adults, sensorimotor beta activity in infants during reaching and grasping movements occurs in bursts, not oscillations like thought traditionally. Furthermore, different beta waveform shapes were differentially modulated with age, including more lateralization in adults. Aberrant beta activity characterizes various developmental disorders and motor difficulties linked to early brain injury, so looking at burst waveform shape could provide more sensitivity for early identification and treatment of affected individuals before any behavioral symptoms emerge. More generally, comparison of beta burst activity in typical versus atypical motor development will also be instrumental in teasing apart the mechanistic functional roles of different types of beta bursts.

Neural tracking measures of speech intelligibility: Manipulating intelligibility while keeping acoustics unchanged

Neural speech tracking has advanced our understanding of how our brains rapidly map an acoustic speech signal onto linguistic representations and ultimately meaning. It remains unclear, however, how speech intelligibility is related to the corresponding neural responses. Many studies addressing this question vary the level of intelligibility by manipulating the acoustic waveform, but this makes it difficult to cleanly disentangle the effects of intelligibility from underlying acoustical confounds. Here, using magnetoencephalography recordings, we study neural measures of speech intelligibility by manipulating intelligibility while keeping the acoustics strictly unchanged. Acoustically identical degraded speech stimuli (three-band noise-vocoded, ~20 s duration) are presented twice, but the second presentation is preceded by the original (nondegraded) version of the speech. This intermediate priming, which generates a "pop-out" percept, substantially improves the intelligibility of the second degraded speech passage. We investigate how intelligibility and acoustical structure affect acoustic and linguistic neural representations using multivariate temporal response functions (mTRFs). As expected, behavioral results confirm that perceived speech clarity is improved by priming. mTRFs analysis reveals that auditory (speech envelope and envelope onset) neural representations are not affected by priming but only by the acoustics of the stimuli (bottom-up driven). Critically, our findings suggest that segmentation of sounds into words emerges with better speech intelligibility, and most strongly at the later (~400 ms latency) word processing stage, in prefrontal cortex, in line with engagement of top-down mechanisms associated with priming. Taken together, our results show that word representations may provide some objective measures of speech comprehension.

The Electrophysiological Markers of Statistically Learned Attentional Enhancement: Evidence for a Saliency-based Mechanism

It is well established that attention can be sharpened through the process of statistical learning (e.g., visual search becomes faster when targets appear at high-relative-to-low probability locations). Although this process of statistically learned attentional enhancement differs behaviorally from the well-studied top-down and bottom-up forms of attention, relatively little work has been done to characterize the electrophysiological correlates of statistically learned attentional enhancement. It thus remains unclear whether statistically learned enhancement recruits any of the same cognitive mechanisms as top-down or bottom-up attention. In the current study, EEG data were collected while participants searched for an ambiguous unique shape in a visual array (the additional singleton task). Unbeknownst to the participants, targets appeared more frequently in one location in space (probability cuing). Encephalographic data were then analyzed in two phases: an anticipatory phase and a reactive phase. In the anticipatory phase preceding search stimuli onset, alpha lateralization as well as the Anterior Directing Attention Negativity and Late Directing Attention Positivity components-signs of preparatory attention known to characterize top-down enhancement-were tested. In the reactive phase, the N2pc component-a well-studied marker of target processing-was examined following stimuli onset. Our results showed that statistically learned attentional enhancement is not characterized by any of the well-known anticipatory markers of top-down attention; yet targets at high probability locations did reliably evoke larger N2pc amplitudes, a finding that is associated with bottom-up attention and saliency. Overall, our findings are consistent with the notion that statistically learned attentional enhancement increases the perceptual salience of items appearing at high-probability locations relative to low-probability locations.

Sound externalization in dynamic binaural listening: A comparative behavioral and EEG study

Binaural reproduction aims at recreating a realistic sound scene at the ears of the listener using headphones. Unfortunately, externalization for frontal and rear sources is often poor (virtual sources are perceived inside the head, instead of outside the head). Nevertheless, previous studies have shown that large head-tracked movements could substantially improve externalization and that this improvement persisted once the subject had stopped moving his/her head. The present study investigates the relation between externalization and evoked response potentials (ERPs) by performing behavioral and EEG measurements in the same experimental conditions. Different degrees of externalization were achieved by preceding measurements with 1) head-tracked movements, 2) untracked head movements, and 3) no head movement. Results showed that performing a head movement, whether the head tracking was active or not, increased the amplitude of ERP components after 100 ms, which suggests that preceding head movements alters the auditory processing. Moreover, untracked head movements gave a stronger amplitude on the N1 component, which might be a marker of a consistency break in regards to the real world. While externalization scores were higher after head-tracked movements in the behavioral experiment, no marker of externalization could be found in the EEG results.

Eelbrain, a Python toolkit for time-continuous analysis with temporal response functions

Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning, and these levels all have distinct but interdependent temporal structures. Time-lagged regression using temporal response functions (TRFs) has recently emerged as a promising tool for disentangling electrophysiological brain responses related to such complex models of perception. Here, we introduce the Eelbrain Python toolkit, which makes this kind of analysis easy and accessible. We demonstrate its use, using continuous speech as a sample paradigm, with a freely available EEG dataset of audiobook listening. A companion GitHub repository provides the complete source code for the analysis, from raw data to group-level statistics. More generally, we advocate a hypothesis-driven approach in which the experimenter specifies a hierarchy of time-continuous representations that are hypothesized to have contributed to brain responses, and uses those as predictor variables for the electrophysiological signal. This is analogous to a multiple regression problem, but with the addition of a time dimension. TRF analysis decomposes the brain signal into distinct responses associated with the different predictor variables by estimating a multivariate TRF (mTRF), quantifying the influence of each predictor on brain responses as a function of time(-lags). This allows asking two questions about the predictor variables: (1) Is there a significant neural representation corresponding to this predictor variable? And if so, (2) what are the temporal characteristics of the neural response associated with it? Thus, different predictor variables can be systematically combined and evaluated to jointly model neural processing at multiple hierarchical levels. We discuss applications of this approach, including the potential for linking algorithmic/representational theories at different cognitive levels to brain responses through computational models with appropriate linking hypotheses.

TMS provokes target-dependent intracranial rhythms across human cortical and subcortical sites

Transcranial magnetic stimulation (TMS) is increasingly deployed in the treatment of neuropsychiatric illness, under the presumption that stimulation of specific cortical targets can alter ongoing neural activity and cause circuit-level changes in brain function. While the electrophysiological effects of TMS have been extensively studied with scalp electroencephalography (EEG), this approach is most useful for evaluating low-frequency neural activity at the cortical surface. As such, little is known about how TMS perturbs rhythmic activity among deeper structures - such as the hippocampus and amygdala - and whether stimulation can alter higher-frequency oscillations. Recent work has established that TMS can be safely used in patients with intracranial electrodes (iEEG), allowing for direct neural recordings at sufficient spatiotemporal resolution to examine localized oscillatory responses across the frequency spectrum. To that end, we recruited 17 neurosurgical patients with indwelling electrodes and recorded neural activity while patients underwent repeated trials of single-pulse TMS at several cortical sites. Stimulation to the dorsolateral prefrontal cortex (DLPFC) drove widespread low-frequency increases (3-8Hz) in frontolimbic cortices, as well as high-frequency decreases (30-110Hz) in frontotemporal areas, including the hippocampus. Stimulation to parietal cortex specifically provoked low-frequency responses in the medial temporal lobe. While most low-frequency activity was consistent with brief evoked responses, anterior frontal regions exhibited induced theta oscillations following DLPFC stimulation. Taken together, we established that non-invasive stimulation can (1) provoke a mixture of low-frequency evoked power and induced theta oscillations and (2) suppress high-frequency activity in deeper brain structures not directly accessed by stimulation itself.

Temporal Dynamics of Brain Activity Predicting Sense of Agency over Muscle Movements

Our muscles are the primary means through which we affect the external world, and the sense of agency (SoA) over the action through those muscles is fundamental to our self-awareness. However, SoA research to date has focused almost exclusively on agency over action outcomes rather than over the musculature itself, as it was believed that SoA over the musculature could not be manipulated directly. Drawing on methods from human-computer interaction and adaptive experimentation, we use human-in-the-loop Bayesian optimization to tune the timing of electrical muscle stimulation so as to robustly elicit a SoA over electrically actuated muscle movements in male and female human subjects. We use time-resolved decoding of subjects' EEG to estimate the time course of neural activity which predicts reported agency on a trial-by-trial basis. Like paradigms which assess SoA over action consequences, we found that the late (post-conscious) neural activity predicts SoA. Unlike typical paradigms, however, we also find patterns of early (sensorimotor) activity with distinct temporal dynamics predicts agency over muscle movements, suggesting that the "neural correlates of agency" may depend on the level of abstraction (i.e., direct sensorimotor feedback versus downstream consequences) most relevant to a given agency judgment. Moreover, fractal analysis of the EEG suggests that SoA-contingent dynamics of neural activity may modulate the sensitivity of the motor system to external input.SIGNIFICANCE STATEMENT The sense of agency, the feeling of "I did that," when directing one's own musculature is a core feature of human experience. We show that we can robustly manipulate the sense of agency over electrically actuated muscle movements, and we investigate the time course of neural activity that predicts the sense of agency over these actuated movements. We find evidence of two distinct neural processes: a transient sequence of patterns that begins in the early sensorineural response to muscle stimulation and a later, sustained signature of agency. These results shed light on the neural mechanisms by which we experience our movements as volitional.

Workload-dependent hemispheric asymmetries during the emotion-cognition interaction: a close-to-naturalistic fNIRS study

Introduction

We investigated brain activation patterns of interacting emotional distractions and cognitive processes in a close-to-naturalistic functional near-infrared spectroscopy (fNIRS) study.

Methods

Eighteen participants engaged in a monitoring-control task, mimicking common air traffic controller requirements. The scenario entailed experiencing both low and high workload, while concurrently being exposed to emotional speech distractions of positive, negative, and neutral valence.

Results

Our investigation identified hemispheric asymmetries in prefrontal cortex (PFC) activity during the presentation of negative and positive emotional speech distractions at different workload levels. Thereby, in particular, activation in the left inferior frontal gyrus (IFG) and orbitofrontal cortex (OFC) seems to play a crucial role. Brain activation patterns revealed a cross-over interaction indicating workload-dependent left hemispheric inhibition processes during negative distractions and high workload. For positive emotional distractions under low workload, we observed left-hemispheric PFC recruitment potentially associated with speech-related processes. Furthermore, we found a workload-independent negativity bias for neutral distractions, showing brain activation patterns similar to those of negative distractions.

Discussion

In conclusion, lateralized hemispheric processing, regulating emotional speech distractions and integrating emotional and cognitive processes, is influenced by workload levels and stimulus characteristics. These findings advance our understanding of the factors modulating hemispheric asymmetries during the processing and inhibition of emotional distractions, as well as the interplay between emotion and cognition. Moreover, they emphasize the significance of exploring emotion-cognition interactions in more naturalistic settings to gain a deeper understanding of their implications in real-world application scenarios (e.g., working and learning environments).

An 8-week compassion and mindfulness-based exposure therapy program improves posttraumatic stress symptoms

The persistence of posttraumatic stress symptoms (PTSS) can be debilitating. However, many people experiencing such symptoms may not qualify for or may not seek treatment. Potentially contributing to ongoing residual symptoms of PTSS is emotion dysregulation. Meanwhile, the research area of mindfulness and compassion has grown to imply emotion regulation as one of its underlying mechanisms; yet, its influence on emotion regulation in PTSS cohort is unknown. Here, we explored the potential effectiveness of an 8-week Compassion-oriented and Mindfulness-based Exposure Therapy (CoMET) for individuals with PTSS using a waitlist control design. A total of 28 individuals (27 females, age range = 18-39 years) participated in the study (17 CoMET; 11 waitlist control). Following CoMET, participants reported significant decreases in PTSS severity (from clinical to non-clinical levels), emotion dysregulation and experiential avoidance, as well as significant increases in mindfulness, self-compassion and quality of life. Electroencephalogram-based brain network connectivity analysis revealed an increase in alpha-band connectivity following CoMET in a network that includes the amygdala, suggesting that CoMET successfully induced changes in functional connectivity between brain regions that play a crucial role in emotion regulation. In sum, the current study demonstrated promising intervention outcomes of CoMET in effectively alleviating the symptoms of PTSS via enhanced emotion regulation.

Magnetoencephalography reveals impaired sensory gating and change detection in older adults in the somatosensory system

Brain electrophysiological responses can provide information about age-related decline in sensory-cognitive functions with high temporal accuracy. Studies have revealed impairments in early sensory gating and pre-attentive change detection mechanisms in older adults, but no magnetoencephalographic (MEG) studies have been undertaken into both non-attentive and attentive somatosensory functions and their relationship to ageing. Magnetoencephalography was utilized to record cortical somatosensory brain responses in young (20-28 yrs), middle-aged (46-56 yrs), and older adults (64-78 yrs) under active and passive somatosensory oddball conditions. A repeated standard stimulus was occasionally replaced by a deviant stimulus (p = .1), which was an electrical pulse on a different finger. We examined the amplitudes of M50 and M100 responses reflecting sensory gating, and later components reflecting change detection and attention shifting (M190 and M250 for the passive condition, and M200 and M350 for the active condition, respectively). Spatiotemporal cluster-based permutation tests revealed that older adults had significantly larger M100 component amplitudes than young adults for task-irrelevant stimuli in both passive and active condition. Older adults also showed a reduced M250 component and an altered M350 in response to deviant stimuli. The responses of middle-aged adults did not differ from those of younger adults, but this study should be repeated with a larger sample size. By demonstrating changes in both somatosensory gating and attentional shifting mechanisms, our findings extend previous research on the effects of ageing on pre-attentive and attentive brain functions.

The effects of trauma on feedback processing: an MEG study

The cognitive impact of psychological trauma can manifest as a range of post-traumatic stress symptoms that are often attributed to impairments in learning from positive and negative outcomes, aka reinforcement learning. Research on the impact of trauma on reinforcement learning has mainly been inconclusive. This study aimed to circumscribe the impact of psychological trauma on reinforcement learning in the context of neural response in time and frequency domains. Two groups of participants were tested - those who had experienced psychological trauma and a control group who had not - while they performed a probabilistic classification task that dissociates learning from positive and negative feedback during a magnetoencephalography (MEG) examination. While the exposure to trauma did not exhibit any effects on learning accuracy or response time for positive or negative feedback, MEG cortical activity was modulated in response to positive feedback. In particular, the medial and lateral orbitofrontal cortices (mOFC and lOFC) exhibited increased activity, while the insular and supramarginal cortices showed decreased activity during positive feedback presentation. Furthermore, when receiving negative feedback, the trauma group displayed higher activity in the medial portion of the superior frontal cortex. The timing of these activity changes occurred between 160 and 600 ms post feedback presentation. Analysis of the time-frequency domain revealed heightened activity in theta and alpha frequency bands (4-10 Hz) in the lOFC in the trauma group. Moreover, dividing the two groups according to their learning performance, the activity for the non-learner subgroup was found to be lower in lOFC and higher in the supramarginal cortex. These differences were found in the trauma group only. The results highlight the localization and neural dynamics of feedback processing that could be affected by exposure to psychological trauma. This approach and associated findings provide a novel framework for understanding the cognitive correlates of psychological trauma in relation to neural dynamics in the space, time, and frequency domains. Subsequent work will focus on the stratification of cognitive and neural correlates as a function of various symptoms of psychological trauma. Clinically, the study findings and approach open the possibility for neuromodulation interventions that synchronize cognitive and psychological constructs for individualized treatment.

Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation

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

Attentional Modulation of the Cortical Contribution to the Frequency-Following Response Evoked by Continuous Speech

Selective attention to one of several competing speakers is required for comprehending a target speaker among other voices and for successful communication with them. It moreover has been found to involve the neural tracking of low-frequency speech rhythms in the auditory cortex. Effects of selective attention have also been found in subcortical neural activities, in particular regarding the frequency-following response related to the fundamental frequency of speech (speech-FFR). Recent investigations have, however, shown that the speech-FFR contains cortical contributions as well. It remains unclear whether these are also modulated by selective attention. Here we used magnetoencephalography to assess the attentional modulation of the cortical contributions to the speech-FFR. We presented both male and female participants with two competing speech signals and analyzed the cortical responses during attentional switching between the two speakers. Our findings revealed robust attentional modulation of the cortical contribution to the speech-FFR: the neural responses were higher when the speaker was attended than when they were ignored. We also found that, regardless of attention, a voice with a lower fundamental frequency elicited a larger cortical contribution to the speech-FFR than a voice with a higher fundamental frequency. Our results show that the attentional modulation of the speech-FFR does not only occur subcortically but extends to the auditory cortex as well.SIGNIFICANCE STATEMENT Understanding speech in noise requires attention to a target speaker. One of the speech features that a listener can use to identify a target voice among others and attend it is the fundamental frequency, together with its higher harmonics. The fundamental frequency arises from the opening and closing of the vocal folds and is tracked by high-frequency neural activity in the auditory brainstem and in the cortex. Previous investigations showed that the subcortical neural tracking is modulated by selective attention. Here we show that attention affects the cortical tracking of the fundamental frequency as well: it is stronger when a particular voice is attended than when it is ignored.

The Characteristics of Power Spectral Density in Bipolar Disorder at the Resting State

Bipolar disorder (BD) is a common psychiatric disorder, but its pathophysiology is not fully elucidated. The current study focused on its electrophysiological characteristics, especially power spectral density (PSD). Resting state with eyes opened magnetoencephalography data were collected from 21 patients with BD and 22 healthy controls. The whole brain's PSD was calculated from source reconstructed waveforms at each frequency band (delta: 1-3 Hz, theta: 4-7 Hz, alpha: 8-12 Hz, low beta: 13-19 Hz, high beta: 20-29 Hz, and gamma: 30-80 Hz). We compared PSD values on the marked vertices at each frequency band between healthy and patient groups using a Mann-Whitney rank test to examine the relationship between significantly different PSD and clinical measures. The PSD in patients with BD was significantly decreased in lower frequency bands, mainly in the default mode network (DMN) areas (bilateral medial prefrontal cortex, bilateral precuneus, left inferior parietal lobe, and right temporal cortex in the alpha band) and salience network areas (SAL; left anterior insula [AI] at the delta band, anterior cingulate cortex at the theta band, and right AI at the alpha band). No significant differences in PSD were observed at low beta and high beta. PSD was not correlated with age or other clinical scales. Altered PSDs of the DMN and SAL were observed in the delta, theta, and alpha bands. These alterations contribute to the vulnerability of BD through the disturbance of self-referential mental activity and switching between the default mode and frontoparietal networks.

The impact of ROI extraction method for MEG connectivity estimation: practical recommendations for the study of resting state data

Magnetoencephalography and electroencephalography (M/EEG) seed-based connectivity analysis requires the extraction of measures from regions of interest (ROI). M/EEG ROI-derived source activity can be treated in different ways. It is possible, for instance, to average each ROI's time series prior to calculating connectivity measures. Alternatively, one can compute connectivity maps for each element of the ROI prior to dimensionality reduction to obtain a single map. The impact of these different strategies on connectivity results is still unclear. Here, we address this question within a large MEG resting state cohort (N=113) and within simulated data. We consider 68 ROIs (Desikan-Kiliany atlas), two measures of connectivity (phase locking value-PLV, and its imaginary counterpart- ciPLV), and three frequency bands (theta 4-8 Hz, alpha 9-12 Hz, beta 15-30 Hz). We compare four extraction methods: (i) mean, or (ii) PCA of the activity within the seed or ROI before computing connectivity, map of the (iii) average, or (iv) maximum connectivity after computing connectivity for each element of the seed. Hierarchical clustering is then applied to compare connectivity outputs across multiple strategies, followed by direct contrasts across extraction methods. Finally, the results are validated by using a set of realistic simulations. We show that ROI-based connectivity maps vary remarkably across strategies in terms of connectivity magnitude and spatial distribution. Dimensionality reduction procedures conducted after computing connectivity are more similar to each-other, while PCA before approach is the most dissimilar to other approaches. Although differences across methods are consistent across frequency bands, they are influenced by the connectivity metric and ROI size. Greater differences were observed for ciPLV than PLV, and in larger ROIs. Realistic simulations confirmed that after aggregation procedures are generally more accurate but have lower specificity (higher rate of false positive connections). Though computationally demanding, after dimensionality reduction strategies should be preferred when higher sensitivity is desired. Given the remarkable differences across aggregation procedures, caution is warranted in comparing results across studies applying different methods.

Divergent auditory activation in relation to inhibition task performance in children and adults

Adults and children show remarkable differences in cortical auditory activation which, in children, have shown relevance for cognitive performance, specifically inhibitory control. However, it has not been tested whether these differences translate to functional differences in response inhibition between adults and children. We recorded auditory responses of adults and school-aged children (6-14 years) using combined magneto- and electroencephalography (M/EEG) during passive listening conditions and an auditory Go/No-go task. The associations between auditory cortical responses and inhibition performance measures diverge between adults and children; while in children the brain-behavior associations are not significant, or stronger responses are beneficial, adults show negative associations between auditory cortical responses and inhibitory performance. Furthermore, we found differences in brain responses between adults and children; the late (~200 ms post stimulation) adult peak activation shifts from auditory to frontomedial areas. In contrast, children show prolonged obligatory responses in the auditory cortex. Together this likely translates to a functional difference between adults and children in the cortical resources for performance consistency in auditory-based cognitive tasks.

Neural Tracking Measures of Speech Intelligibility: Manipulating Intelligibility while Keeping Acoustics Unchanged

Neural speech tracking has advanced our understanding of how our brains rapidly map an acoustic speech signal onto linguistic representations and ultimately meaning. It remains unclear, however, how speech intelligibility is related to the corresponding neural responses. Many studies addressing this question vary the level of intelligibility by manipulating the acoustic waveform, but this makes it difficult to cleanly disentangle effects of intelligibility from underlying acoustical confounds. Here, using magnetoencephalography (MEG) recordings, we study neural measures of speech intelligibility by manipulating intelligibility while keeping the acoustics strictly unchanged. Acoustically identical degraded speech stimuli (three-band noise vocoded, ~20 s duration) are presented twice, but the second presentation is preceded by the original (non-degraded) version of the speech. This intermediate priming, which generates a 'pop-out' percept, substantially improves the intelligibility of the second degraded speech passage. We investigate how intelligibility and acoustical structure affects acoustic and linguistic neural representations using multivariate Temporal Response Functions (mTRFs). As expected, behavioral results confirm that perceived speech clarity is improved by priming. TRF analysis reveals that auditory (speech envelope and envelope onset) neural representations are not affected by priming, but only by the acoustics of the stimuli (bottom-up driven). Critically, our findings suggest that segmentation of sounds into words emerges with better speech intelligibility, and most strongly at the later (~400 ms latency) word processing stage, in prefrontal cortex (PFC), in line with engagement of top-down mechanisms associated with priming. Taken together, our results show that word representations may provide some objective measures of speech comprehension.

EEG functional connectivity in infants at elevated familial likelihood for autism spectrum disorder

BACKGROUND

Many studies have reported that autism spectrum disorder (ASD) is associated with atypical structural and functional connectivity. However, we know relatively little about the development of these differences in infancy.

METHODS

We used a high-density electroencephalogram (EEG) dataset pooled from two independent infant sibling cohorts, to characterize such neurodevelopmental deviations during the first years of life. EEG was recorded at 6 and 12 months of age in infants at typical (N = 92) or elevated likelihood for ASD (N = 90), determined by the presence of an older sibling with ASD. We computed the functional connectivity between cortical sources of EEG during video watching using the corrected imaginary part of phase-locking values.

RESULTS

Our main analysis found no significant association between functional connectivity and ASD, showing only significant effects for age, sex, age-sex interaction, and site. Given these null results, we performed an exploratory analysis and observed, at 12 months, a negative correlation between functional connectivity and ADOS calibrated severity scores for restrictive and repetitive behaviors (RRB).

LIMITATIONS

The small sample of ASD participants inherent to sibling studies limits diagnostic group comparisons. Also, results from our secondary exploratory analysis should be considered only as potential relationships to further explore, given their increased vulnerability to false positives.

CONCLUSIONS

These results are inconclusive concerning an association between EEG functional connectivity and ASD in infancy. Exploratory analyses provided preliminary support for a relationship between RRB and functional connectivity specifically, but these preliminary observations need corroboration on larger samples.

Single-trial classification of evoked responses to auditory tones using OPM- and SQUID-MEG

Objective.Optically pumped magnetometers (OPMs) are emerging as a near-room-temperature alternative to superconducting quantum interference devices (SQUIDs) for magnetoencephalography (MEG). In contrast to SQUIDs, OPMs can be placed in a close proximity to subject's scalp potentially increasing the signal-to-noise ratio and spatial resolution of MEG. However, experimental demonstrations of these suggested benefits are still scarce. Here, to compare a 24-channel OPM-MEG system to a commercial whole-head SQUID system in a data-driven way, we quantified their performance in classifying single-trial evoked responses.Approach.We measured evoked responses to three auditory tones in six participants using both OPM- and SQUID-MEG systems. We performed pairwise temporal classification of the single-trial responses with linear discriminant analysis as well as multiclass classification with both EEGNet convolutional neural network and xDAWN decoding.Main results.OPMs provided higher classification accuracies than SQUIDs having a similar coverage of the left hemisphere of the participant. However, the SQUID sensors covering the whole helmet had classification scores larger than those of OPMs for two of the tone pairs, demonstrating the benefits of a whole-head measurement.Significance.The results demonstrate that the current OPM-MEG system provides high-quality data about the brain with room for improvement for high bandwidth non-invasive brain-computer interfacing.

Generation of synthetic EEG data for training algorithms supporting the diagnosis of major depressive disorder

Introduction

Major depressive disorder (MDD) is the most common mental disorder worldwide, leading to impairment in quality and independence of life. Electroencephalography (EEG) biomarkers processed with machine learning (ML) algorithms have been explored for objective diagnoses with promising results. However, the generalizability of those models, a prerequisite for clinical application, is restricted by small datasets. One approach to train ML models with good generalizability is complementing the original with synthetic data produced by generative algorithms. Another advantage of synthetic data is the possibility of publishing the data for other researchers without risking patient data privacy. Synthetic EEG time-series have not yet been generated for two clinical populations like MDD patients and healthy controls.

Methods

We first reviewed 27 studies presenting EEG data augmentation with generative algorithms for classification tasks, like diagnosis, for the possibilities and shortcomings of recent methods. The subsequent empirical study generated EEG time-series based on two public datasets with 30/28 and 24/29 subjects (MDD/controls). To obtain baseline diagnostic accuracies, convolutional neural networks (CNN) were trained with time-series from each dataset. The data were synthesized with generative adversarial networks (GAN) consisting of CNNs. We evaluated the synthetic data qualitatively and quantitatively and finally used it for re-training the diagnostic model.

Results

The reviewed studies improved their classification accuracies by between 1 and 40% with the synthetic data. Our own diagnostic accuracy improved up to 10% for one dataset but not significantly for the other. We found a rich repertoire of generative models in the reviewed literature, solving various technical issues. A major shortcoming in the field is the lack of meaningful evaluation metrics for synthetic data. The few studies analyzing the data in the frequency domain, including our own, show that only some features can be produced truthfully.

Discussion

The systematic review combined with our own investigation provides an overview of the available methods for generating EEG data for a classification task, their possibilities, and shortcomings. The approach is promising and the technical basis is set. For a broad application of these techniques in neuroscience research or clinical application, the methods need fine-tuning facilitated by domain expertise in (clinical) EEG research.

Speaker-induced Suppression in EEG during a Naturalistic Reading and Listening Task

Speaking elicits a suppressed neural response when compared with listening to others' speech, a phenomenon known as speaker-induced suppression (SIS). Previous research has focused on investigating SIS at constrained levels of linguistic representation, such as the individual phoneme and word level. Here, we present scalp EEG data from a dual speech perception and production task where participants read sentences aloud then listened to playback of themselves reading those sentences. Playback was separated into immediate repetition of the previous trial and randomized repetition of a former trial to investigate if forward modeling of responses during passive listening suppresses the neural response. Concurrent EMG was recorded to control for movement artifact during speech production. In line with previous research, ERP analyses at the sentence level demonstrated suppression of early auditory components of the EEG for production compared with perception. To evaluate whether linguistic abstractions (in the form of phonological feature tuning) are suppressed during speech production alongside lower-level acoustic information, we fit linear encoding models that predicted scalp EEG based on phonological features, EMG activity, and task condition. We found that phonological features were encoded similarly between production and perception. However, this similarity was only observed when controlling for movement by using the EMG response as an additional regressor. Our results suggest that SIS operates at a sensory representational level and is dissociated from higher order cognitive and linguistic processing that takes place during speech perception and production. We also detail some important considerations when analyzing EEG during continuous speech production.

Neural electrophysiological correlates of detection and identification awareness

Humans have conscious experiences of the events in their environment. Previous research from electroencephalography (EEG) has shown visual awareness negativity (VAN) at about 200 ms to be a neural correlate of consciousness (NCC). However, when considering VAN as an NCC, it is important to explore which particular experiences are associated with VAN. Recent research proposes that VAN is an NCC of lower-level experiences (detection) rather than higher-level experiences (identification). However, previous results are mixed and have several limitations. In the present study, the stimulus was a ring with a Gabor patch tilting either left or right. On each trial, subjects rated their awareness on a three-level perceptual awareness scale that captured both detection (something vs. nothing) and identification (identification vs. something). Separate staircases were used to adjust stimulus opacity to the detection threshold and the identification threshold. Bayesian linear mixed models provided extreme evidence (BF10 = 131) that VAN was stronger at the detection threshold than at the identification threshold. Mean VAN decreased from [Formula: see text]2.12 microV [[Formula: see text]2.86, [Formula: see text]1.42] at detection to [Formula: see text]0.46 microV [[Formula: see text]0.79, [Formula: see text]0.11] at identification. These results strongly support the claim that VAN is an NCC of lower-level experiences of seeing something rather than of higher-level experiences of specific properties of the stimuli. Thus, results are consistent with recurrent processing theory in that phenomenal visual consciousness is reflected by VAN. Further, results emphasize that it is important to consider the level of experience when searching for NCC.

The influence of inter-regional delays in generating large-scale brain networks of phase synchronization

Large-scale networks of phase synchronization are considered to regulate the communication between brain regions fundamental to cognitive function, but the mapping to their structural substrates, i.e., the structure-function relationship, remains poorly understood. Biophysical Network Models (BNMs) have demonstrated the influences of local oscillatory activity and inter-regional anatomical connections in generating alpha-band (8-12 Hz) networks of phase synchronization observed with Electroencephalography (EEG) and Magnetoencephalography (MEG). Yet, the influence of inter-regional conduction delays remains unknown. In this study, we compared a BNM with standard "distance-dependent delays", which assumes constant conduction velocity, to BNMs with delays specified by two alternative methods accounting for spatially varying conduction velocities, "isochronous delays" and "mixed delays". We followed the Approximate Bayesian Computation (ABC) workflow, i) specifying neurophysiologically informed prior distributions of BNM parameters, ii) verifying the suitability of the prior distributions with Prior Predictive Checks, iii) fitting each of the three BNMs to alpha-band MEG resting-state data (N = 75) with Bayesian optimization for Likelihood-Free Inference (BOLFI), and iv) choosing between the fitted BNMs with ABC model comparison on a separate MEG dataset (N = 30). Prior Predictive Checks revealed the range of dynamics generated by each of the BNMs to encompass those seen in the MEG data, suggesting the suitability of the prior distributions. Fitting the models to MEG data yielded reliable posterior distributions of the parameters of each of the BNMs. Finally, model comparison revealed the BNM with "distance-dependent delays", as the most probable to describe the generation of alpha-band networks of phase synchronization seen in MEG. These findings suggest that distance-dependent delays might contribute to the neocortical architecture of human alpha-band networks of phase synchronization. Hence, our study illuminates the role of inter-regional delays in generating the large-scale networks of phase synchronization that might subserve the communication between regions vital to cognition.

A high-density EEG and structural MRI source analysis of the frequency following response to missing fundamental stimuli reveals subcortical and cortical activation to low and high frequency stimuli

Pitch is a perceptual rather than physical phenomenon, important for spoken language use, musical communication, and other aspects of everyday life. Auditory stimuli can be designed to probe the relationship between perception and physiological responses to pitch-evoking stimuli. One technique for measuring physiological responses to pitch-evoking stimuli is the frequency following response (FFR). The FFR is an electroencephalographic (EEG) response to periodic auditory stimuli. The FFR contains nonlinearities not present in the stimuli, including correlates of the amplitude envelope of the stimulus; however, these nonlinearities remain undercharacterized. The FFR is a composite response reflecting multiple neural and peripheral generators, and their contributions to the scalp-recorded FFR vary in ill-understood ways depending on the electrode montage, stimulus, and imaging technique. The FFR is typically assumed to be generated in the auditory brainstem; there is also evidence both for and against a cortical contribution to the FFR. Here a methodology is used to examine the FFR correlates of pitch and the generators of the FFR to stimuli with different pitches. Stimuli were designed to tease apart biological correlates of pitch and amplitude envelope. FFRs were recorded with 256-electrode EEG nets, in contrast to a typical FFR setup which only contains a single active electrode. Structural MRI scans were obtained for each participant to co-register with the electrode locations and constrain a source localization algorithm. The results of this localization shed light on the generating mechanisms of the FFR, including providing evidence for both cortical and subcortical auditory sources.

Assessing focus through ear-EEG: a comparative study between conventional cap EEG and mobile in- and around-the-ear EEG systems

Introduction

As our attention is becoming a commodity that an ever-increasing number of applications are competing for, investing in modern day tools and devices that can detect our mental states and protect them from outside interruptions holds great value. Mental fatigue and distractions are impacting our ability to focus and can cause workplace injuries. Electroencephalography (EEG) may reflect concentration, and if EEG equipment became wearable and inconspicuous, innovative brain-computer interfaces (BCI) could be developed to monitor mental load in daily life situations. The purpose of this study is to investigate the potential of EEG recorded inside and around the human ear to determine levels of attention and focus.

Methods

In this study, mobile and wireless ear-EEG were concurrently recorded with conventional EEG (cap) systems to collect data during tasks related to focus: an N-back task to assess working memory and a mental arithmetic task to assess cognitive workload. The power spectral density (PSD) of the EEG signal was analyzed to isolate consistent differences between mental load conditions and classify epochs using step-wise linear discriminant analysis (swLDA).

Results and discussion

Results revealed that spectral features differed statistically between levels of cognitive load for both tasks. Classification algorithms were tested on spectral features from twelve and two selected channels, for the cap and the ear-EEG. A two-channel ear-EEG model evaluated the performance of two dry in-ear electrodes specifically. Single-trial classification for both tasks revealed above chance-level accuracies for all subjects, with mean accuracies of: 96% (cap-EEG) and 95% (ear-EEG) for the twelve-channel models, 76% (cap-EEG) and 74% (in-ear-EEG) for the two-channel model for the N-back task; and 82% (cap-EEG) and 85% (ear-EEG) for the twelve-channel, 70% (cap-EEG) and 69% (in-ear-EEG) for the two-channel model for the arithmetic task. These results suggest that neural oscillations recorded with ear-EEG can be used to reliably differentiate between levels of cognitive workload and working memory, in particular when multi-channel recordings are available, and could, in the near future, be integrated into wearable devices.

Method for Assessing the Influence of Phobic Stimuli in Virtual Simulators

In the organizing of professional training, the assessment of the trainee's reaction and state in stressful situations is of great importance. Phobic reactions are a specific type of stress reaction that, however, is rarely taken into account when developing virtual simulators, and are a risk factor in the workplace. A method for evaluating the impact of various phobic stimuli on the quality of training is considered, which takes into account the time, accuracy, and speed of performing professional tasks, as well as the characteristics of electroencephalograms (the amplitude, power, coherence, Hurst exponent, and degree of interhemispheric asymmetry). To evaluate the impact of phobias during experimental research, participants in the experimental group performed exercises in different environments: under normal conditions and under the influence of acrophobic and arachnophobic stimuli. The participants were divided into subgroups using clustering algorithms and an expert neurologist. After that, a comparison of the subgroup metrics was carried out. The research conducted makes it possible to partially confirm our hypotheses about the negative impact of phobic effects on some participants in the experimental group. The relationship between the reaction to a phobia and the characteristics of brain activity was revealed, and the characteristics of the electroencephalogram signal were considered as the metrics for detecting a phobic reaction.