Recorded Brain Activity Research Articles

RECOGNITION OF ORAL SPEECH ACCORDING TO MEG DATA BY COVARIANCE FILTERS

Speech recognition based on EEG and MEG data is the first step in the development of BCI and AI systems for their further use in inner speech decoding. Great advances in this direction have been made using ECoG and stereo-EEG. At the same time, there are few works on this topic on the analysis of data obtained by non-invasive methods of recording brain activity. Our approach is based on the evaluation of connections in the space of sensors with the identification of a pattern of MEG connectivity specific for a given segment of speech. We tested our method on 7 subjects. In all cases, our processing pipeline was quite reliable and worked either without recognition errors or with a small number of errors. After “training”, the algorithm is able to recognise a fragment of oral speech with a single presentation. For recognition, we used segments of the MEG recording 50–1200 ms from the beginning of the sound of the word. For high-quality recognition, a segment of at least 600 ms was required. Intervals longer than 1200 ms worsened the recognition quality. Bandpass filtering of the MEG showed that the quality of recognition is equally effective in the entire frequency range. Some decrease in the level of recognition is observed only in the range of 9–14 Hz.

Large-scale recording of neuronal activity in freely-moving mice at cellular resolution

Current methods for recording large-scale neuronal activity from behaving mice at single-cell resolution require either fixing the mouse head under a microscope or attachment of a recording device to the animal’s skull. Both of these options significantly affect the animal behavior and hence also the recorded brain activity patterns. Here, we introduce a different method to acquire snapshots of single-cell cortical activity maps from freely-moving mice using a calcium sensor called CaMPARI. CaMPARI has a unique property of irreversibly changing its color from green to red inside active neurons when illuminated with 400 nm light. We capitalize on this property to demonstrate cortex-wide activity recording without any head fixation, tethering, or attachment of a miniaturized device to the mouse’s head. Multiple cortical regions were recorded while the mouse was performing a battery of behavioral and cognitive tests. We identified task-dependent activity patterns across motor and somatosensory cortices, with significant differences across sub-regions of the motor cortex and correlations across several activity patterns and task parameters. This CaMPARI-based recording method expands the capabilities of recording neuronal activity from freely-moving and behaving mice under minimally-restrictive experimental conditions and provides large-scale volumetric data that are currently not accessible otherwise.

Implantable Multimodal Sensing Device for Simultaneous Imaging and Electrophysiological Recording of Mouse Brain Activity

Implantable Multimodal Sensing Device for Simultaneous Imaging and Electrophysiological Recording of Mouse Brain Activity

Dual-Guided Brain Diffusion Model: Natural Image Reconstruction from Human Visual Stimulus fMRI.

The reconstruction of visual stimuli from fMRI signals, which record brain activity, is a challenging task with crucial research value in the fields of neuroscience and machine learning. Previous studies tend to emphasize reconstructing pixel-level features (contours, colors, etc.) or semantic features (object category) of the stimulus image, but typically, these properties are not reconstructed together. In this context, we introduce a novel three-stage visual reconstruction approach called the Dual-guided Brain Diffusion Model (DBDM). Initially, we employ the Very Deep Variational Autoencoder (VDVAE) to reconstruct a coarse image from fMRI data, capturing the underlying details of the original image. Subsequently, the Bootstrapping Language-Image Pre-training (BLIP) model is utilized to provide a semantic annotation for each image. Finally, the image-to-image generation pipeline of the Versatile Diffusion (VD) model is utilized to recover natural images from the fMRI patterns guided by both visual and semantic information. The experimental results demonstrate that DBDM surpasses previous approaches in both qualitative and quantitative comparisons. In particular, the best performance is achieved by DBDM in reconstructing the semantic details of the original image; the Inception, CLIP and SwAV distances are 0.611, 0.225 and 0.405, respectively. This confirms the efficacy of our model and its potential to advance visual decoding research.

Neural fingerprinting on MEG time series using MiniRocket.

Neural fingerprinting is the identification of individuals in a cohort based on neuroimaging recordings of brain activity. In magneto- and electroencephalography (M/EEG), it is common practice to use second-order statistical measures, such as correlation or connectivity matrices, when neural fingerprinting is performed. These measures or features typically require coupling between signal channels and often ignore the individual temporal dynamics. In this study, we show that, following recent advances in multivariate time series classification, such as the development of the RandOm Convolutional KErnel Transformation (ROCKET) classifier, it is possible to perform classification directly on short time segments from MEG resting-state recordings with remarkably high classification accuracies. In a cohort of 124 subjects, it was possible to assign windows of time series of 1 s in duration to the correct subject with above 99% accuracy. The achieved accuracies are vastly superior to those of previous methods while simultaneously requiring considerably shorter time segments.

Mild cognitive impairment detection with optimally selected EEG channels based on variational mode decomposition and supervised machine learning

Detecting mild cognitive impairment (MCI), which is typically the earliest stage of dementia, is essential for managing dementia. Recently, researchers have explored the use of electroencephalography (EEG) to detect MCI. EEG involves recording brain activity by placing a number of electrodes (channels) directly on the scalp. Here we present effective variational mode decomposition (VMD)-based methods for detection of MCI with optimally selected EEG channels. To achieve this, we first decompose the EEG signals from each channel into sub-signals called variational mode functions (VMFs) with different frequency bands. From each VMF, an entropy measure is used to extract a feature. Several supervised machine learning techniques are investigated to classify the resulting features and obtain full-channel classification accuracy. We assess the classification performance of the proposed methods on 19-channel EEGs recorded from 32 healthy subjects and 29 MCI patients in the resting state. In a second step, we explore the feasibility of reducing the number of EEG channels while maintaining classification accuracy by using several EEG channel selection methods. Specifically, we study the effectiveness of non-dominated sorting genetic algorithm (NSGA)-based methods (multi-objective optimization methods), in addition to greedy algorithms such as back-elimination and forward-addition. Results show that selecting a few suitable channels can lead to similar or even superior accuracy compared to a classifier that utilizes all EEG channels. The results also show that the multi-objective NSGA optimization methods have a greater ability than the greedy methods to select few compatible channels. For example, 99.51% is the maximum accuracy achieved using all 19 EEG channels when processed with a combination of VMD, log energy entropy, and a K-nearest neighbor (KNN) classifier. The same accuracy is achieved with only 9 channels selected by NSGA, compared to 13 channels for greedy algorithms. With an ensemble KNN classifier, the highest accuracy, 99.81%, is achieved using 11 channels selected by an NSGA-based method. The results are encouraging, demonstrating that this brain disorder can be accurately diagnosed using a minimal number of electrodes and paving the way for its adoption in clinical practice.

Where in the brain do internally generated and externally presented visual information interact?

Conscious experiences normally result from the flow of external input into our sensory systems. However, we can also create conscious percepts independently of sensory stimulation. These internally generated percepts are referred to as mental images, and they have many similarities with real visual percepts. Consequently, mental imagery is often referred to as “seeing in the mind’s eye”. While the neural basis of imagery has been widely studied, the interaction between internal and external sources of visual information has received little interest. Here we examined this question by using fMRI to record brain activity of healthy human volunteers while they were performing visual imagery that was distracted with a concurrent presentation of a visual stimulus. Multivariate pattern analysis (MVPA) was used to identify the brain basis of this interaction. Visual imagery was reflected in several brain areas in ventral temporal, lateral occipitotemporal, and posterior frontal cortices, with a left-hemisphere dominance. The key finding was that imagery content representations in the left lateral occipitotemporal cortex were disrupted when a visual distractor was presented during imagery. Our results thus demonstrate that the representations of internal and external visual information interact in brain areas associated with the encoding of visual objects and shapes.

Open access dataset integrating EEG and fNIRS during Stroop tasks

Conflict monitoring and processing are crucial components of the human cognitive system, with significant implications for daily life and the diagnosis of cognitive disorders. The Stroop task, combined with brain function detection technology, has been widely employed as a classical paradigm for investigating conflict processing. However, there remains a lack of public datasets that integrate Electroencephalogram (EEG) and functional Near-infrared Spectroscopy (fNIRS) to simultaneously record brain activity during a Stroop task. We introduce a dual-modality Stroop task dataset incorporating 34-channel EEG (sampling frequency is 1000 Hz) and 20-channel high temporal resolution fNIRS (sampling frequency is 100 Hz) measurements covering the whole frontal cerebral cortex from 21 participants (9 females/12 males, aged 23.0 ± 2.3 years). Event-related potential analysis of EEG recordings and activation analysis of fNIRS recordings were performed to show the significant Stroop effect. We expected that the data provided would be utilized to investigate multimodal data processing algorithms during cognitive processing.

MICROSTATELAB: The EEGLAB Toolbox for Resting-State Microstate Analysis

Microstate analysis is a multivariate method that enables investigations of the temporal dynamics of large-scale neural networks in EEG recordings of human brain activity. To meet the enormously increasing interest in this approach, we provide a thoroughly updated version of the first open source EEGLAB toolbox for the standardized identification, visualization, and quantification of microstates in resting-state EEG data. The toolbox allows scientists to (i) identify individual, mean, and grand mean microstate maps using topographical clustering approaches, (ii) check data quality and detect outlier maps, (iii) visualize, sort, and label individual, mean, and grand mean microstate maps according to published maps, (iv) compare topographical similarities of group and grand mean microstate maps and quantify shared variances, (v) obtain the temporal dynamics of the microstate classes in individual EEGs, (vi) export quantifications of these temporal dynamics of the microstates for statistical tests, and finally, (vii) test for topographical differences between groups and conditions using topographic analysis of variance (TANOVA). Here, we introduce the toolbox in a step-by-step tutorial, using a sample dataset of 34 resting-state EEG recordings that are publicly available to follow along with this tutorial. The goals of this manuscript are (a) to provide a standardized, freely available toolbox for resting-state microstate analysis to the scientific community, (b) to allow researchers to use best practices for microstate analysis by following a step-by-step tutorial, and (c) to improve the methodological standards of microstate research by providing previously unavailable functions and recommendations on critical decisions required in microstate analyses.

Individual prediction tendencies do not generalize across modalities.

Predictive processing theories, which model the brain as a "prediction machine", explain a wide range of cognitive functions, including learning, perception and action. Furthermore, it is increasingly accepted that aberrant prediction tendencies play a crucial role in psychiatric disorders. Given this explanatory value for clinical psychiatry, prediction tendencies are often implicitly conceptualized as individual traits or as tendencies that generalize across situations. As this has not yet explicitly been shown, in the current study, we quantify to what extent the individual tendency to anticipate sensory features of high probability generalizes across modalities. Using magnetoencephalography (MEG), we recorded brain activity while participants were presented with a sequence of four different (either visual or auditory) stimuli, which changed according to predefined transitional probabilities of two entropy levels: ordered vs. random. Our results show that, on a group-level, under conditions of low entropy, stimulus features of high probability are preactivated in the auditory but not in the visual modality. Crucially, the magnitude of the individual tendency to predict sensory events seems not to correlate between the two modalities. Furthermore, reliability statistics indicate poor internal consistency, suggesting that the measures from the different modalities are unlikely to reflect a single, common cognitive process. In sum, our findings suggest that quantification and interpretation of individual prediction tendencies cannot be generalized across modalities.

Both stronger and weaker cerebro-cerebellar functional connectivity patterns during processing of spoken sentences in autism spectrum disorder.

Cerebellar differences have long been documented in autism spectrum disorder (ASD), yet the extent to which such differences might impact language processing in ASD remains unknown. To investigate this, we recorded brain activity with magnetoencephalography (MEG) while ASD and age-matched typically developing (TD) children passively processed spoken meaningful English and meaningless Jabberwocky sentences. Using a novel source localization approach that allows higher resolution MEG source localization of cerebellar activity, we found that, unlike TD children, ASD children showed no difference between evoked responses to meaningful versus meaningless sentences in right cerebellar lobule VI. ASD children also had atypically weak functional connectivity in the meaningful versus meaningless speech condition between right cerebellar lobule VI and several left-hemisphere sensorimotor and language regions in later time windows. In contrast, ASD children had atypically strong functional connectivity for in the meaningful versus meaningless speech condition between right cerebellar lobule VI and primary auditory cortical areas in an earlier time window. The atypical functional connectivity patterns in ASD correlated with ASD severity and the ability to inhibit involuntary attention. These findings align with a model where cerebro-cerebellar speech processing mechanisms in ASD are impacted by aberrant stimulus-driven attention, which could result from atypical temporal information and predictions of auditory sensory events by right cerebellar lobule VI.

Scale-free correlations in the dynamics of a small (N∼10000) cortical network.

The advent of novel optogenetics technology allows the recording of brain activity with a resolution never seen before. The characterization of these very large data sets offers new challenges as well as unique theory-testing opportunities. Here we discuss whether the spatial and temporal correlations of the collective activity of thousands of neurons are tangled as predicted by the theory of critical phenomena. The analysis shows that both the correlation length ξ and the correlation time τ scale as predicted as a function of the system size. With some peculiarities that we discuss, the analysis uncovers evidence consistent with the view that the large-scale brain cortical dynamics corresponds to critical phenomena.

Prefrontal oxygenation varies as a function of response inhibition performance in healthy participants but not in youth with non-suicidal self-injury.

Non-suicidal self-injury (NSSI), a highly prevalent symptom in adolescence, has been associated with impulsivity. Behavioral measures of response inhibition in combination with the recording of brain activity potentially improve the understanding of the etiology of the behavior. We therefore investigated prefrontal cortex (PFC) oxygenation during a response inhibition task using functional near-infrared spectroscopy (fNIRS) in n=152 adolescents with NSSI and n=47 healthy controls. We compared groups regarding behavioral performance and PFC oxygenation and tested whether the association between task performance and PFC oxygenation differed between groups. PFC oxygenation was slightly higher in adolescents with NSSI than in controls. Further, there was evidence for a group by performance interaction: In healthy controls, higher oxygenated hemoglobin was associated with better task performance, which was not the case in the NSSI group. We did not find evidence of associations between PFC oxygenation and clinical measures. Our study provides preliminary evidence of altered brain functional correlates of response inhibition in adolescents with NSSI potentially reflecting deficient top-down regulation of limbic regions through prefrontal regions. Due to methodological limitations of the current study, findings must be interpreted with caution and future studies should optimize task designs for fNIRS processing.

The role of outgroup homogeneity and the neurodynamics of the frontal cortex during beauty comparisons

ABSTRACTThe distance effect states that the closer two compared magnitudes (e.g., two numbers, physical attractiveness in two faces), the more difficult the comparison, and the greater the activity of the frontoparietal control network. However, it is unclear whether this network is also recruited to the same extent when we perform ingroup and outgroup beauty comparisons and whether the activation of these networks is tracked by interindividual variation in the perceptions we hold about an outgroup. We recorded brain activity with fMRI, where participants compared the beauty of two women ostensibly either from their ingroup or from an outgroup. Low-distance conditions produced longer response times than the high-distance conditions, and this was found in both the ingroup and outgroup conditions. However, our neuroimaging analyses revealed that the left IFG/anterior insula showed the classic distance effect only during ingroup processing but not during outgroup processing. Notably, interaction-specific activity within the left IFG/anterior insula was related to perceptions of outgroup homogeneity assessed via a questionnaire. This set of findings reveals the dynamic role of the prefrontal cortex and its interplay with perceptions of outgroup homogeneity in shaping ingroup and outgroup decision-making.

Introducing a New Mobile Electroencephalography System and Evaluating Its Quality in Comparison to Clinical Electroencephalography.

Electroencephalography (EEG) is a crucial tool in cognitive neuroscience, enabling the study of neurophysiological function by measuring the brain's electrical activity. Its applications include perception, learning, memory, language, decision making and neural network mapping. Recently, interest has surged in extending EEG measurements to domestic environments. However, the high costs associated with traditional laboratory EEG systems have hindered accessibility for many individuals and researchers in education, research, and medicine. To tackle this, a mobile-EEG device named "DreamMachine" was developed. A more affordable alternative to both lab-based EEG systems and existing mobile-EEG devices. This system boasts 24 channels, 24-bit resolution, up to 6 h of battery life, portability, and a low price. Our open-source and open-hardware approach empowers cognitive neuroscience, especially in education, learning, and research, opening doors to more accessibility. This paper introduces the DreamMachine's design and compares it with the lab-based EEG system "asalabTM" in an eyes-open and eyes-closed experiment. The Alpha band exhibited higher power in the power spectrum during eyes-closed conditions, whereas the eyes-open condition showed increased power specifically within the Delta frequency range. Our analysis confirms that the DreamMachine accurately records brain activity, meeting the necessary standards when compared to the asalabTM system.

Multifunctional Fiber-Based Optoacoustic Emitter as a Bidirectional Brain Interface.

A bidirectional brain interface with both "write" and "read" functions can be an important tool for fundamental studies and potential clinical treatments for neurological diseases. Herein, a miniaturized multifunctional fiber-based optoacoustic emitter (mFOE) is reported thatintegrates simultaneous optoacoustic stimulation for "write" and electrophysiology recording of neural circuits for "read". Because of the intrinsic ability of neurons to respond to acoustic wave, there is no requirement of the viral transfection. The orthogonality between optoacoustic waves and electrical field provides a solution to avoid the interference between electrical stimulation and recording. The stimulation function of the mFOE is first validated in cultured ratcortical neurons using calcium imaging. In vivo application of mFOE for successful simultaneous optoacoustic stimulation and electrical recording of brain activities is confirmed in mouse hippocampus in both acute and chronical applications up to 1 month. Minor brain tissue damage is confirmed after these applications. The capability of simultaneous neural stimulation and recording enabled by mFOE opens up new possibilities for the investigation of neural circuits and brings new insights into the study of ultrasound neurostimulation.

Two-Photon Calcium Imaging of Forebrain Activity in Behaving Adult Zebrafish.

Adult zebrafish (Danio rerio) exhibit a rich repertoire of behaviors for studying cognitive functions. They also have a miniature brain that can be used for measuring activities across brain regions through optical imaging methods. However, reports on the recording of brain activity in behaving adult zebrafish have been scarce. The present study describes procedures to perform two-photon calcium imaging in the dorsal forebrain of adult zebrafish. We focus on steps to restrain adult zebrafish from moving their heads, which provides stability that enables laser scanning imaging of the brain activity. The head-restrained animals can freely move their body parts and breathe without aids. The procedure aims to shorten the time of head restraint surgery, minimize brain motion, and maximize the number of neurons recorded. A setup for presenting an immersive visual environment during calcium imaging is also described here, which can be used to study neural correlates underlying visually triggered behaviors.

Evidence accumulation rate moderates the relationship between enriched environment exposure and age-related response speed declines.

Older adults exposed to enriched environments (EE) maintain relatively higher levels of cognitive function, even in the face of compromised markers of brain health. Response speed (RS) is often used as a simple proxy to measure the preservation of global cognitive function in older adults. However, it is unknown which specific selection, decision, and/or motor processes provide the most specific indices of neurocognitive health. Here, using a simple decision task with electroencephalography (EEG), we found that the efficiency with which an individual accumulates sensory evidence was a critical determinant of the extent to which RS was preserved in older adults (63% female, 37% male). Moreover, the mitigating influence of EE on age-related RS declines was most pronounced when evidence accumulation rates were shallowest. These results suggest that the phenomenon of cognitive reserve, whereby high EE individuals can better tolerate suboptimal brain health to facilitate the preservation of cognitive function, is not just applicable to neuroanatomical indicators of brain ageing, but can be observed in markers of neurophysiology. Our results suggest that EEG metrics of evidence accumulation may index neurocognitive vulnerability of the ageing brain.SIGNIFICANCE STATEMENT:Response speed in older adults is closely linked with trajectories of cognitive ageing. Here, by recording brain activity while individuals perform a simple computer task, we identify a neural metric which is a critical determinant of response speed. Older adults exposed to greater cognitive and social stimulation throughout a lifetime could maintain faster responding, even when this neural metric was impaired. This work suggests EEG is a useful technique for interrogating how a lifetime of stimulation benefits brain health in ageing.

Tunable organic active neural probe enabling near-sensor signal processing.

Neural recording systems have significantly progressed to provide an advanced understanding and treatment for neurological diseases. Flexible transistor-based active neural probes exhibit great potential in electrophysiology applications due to their intrinsic amplification capability and tissue-compliant nature. However, most current active neural probes exhibit bulky back-end connectivity since the output is current, and the development of an integrated circuit for voltage output is crucial for near-sensor signal processing at the abiotic/biotic interface. Here, wepresent inkjet-printed organic voltage amplifiers by monolithically integrating organic electrochemical transistors and thin-film polymer resistors on a single, highly flexible substrate for in vivo brain activity recording. Additive inkjet printing enables the seamless integration of multiple active and passive components on the somatosensory cortex, leading to significant noise reduction over the externally connected typical configuration. It also facilitates fine-tuning of the voltage amplification and frequency properties. Wevalidate the organic voltage amplifiers as electrocorticography devices in a rat in vivo model, showing their ability to record local field potentials in an experimental model of spontaneous and epileptiform activity. Ourresults bring organic active neural probes to the forefront in applications where efficient sensory data processing is performed at sensor endpoints. This article is protected by copyright. All rights reserved.

BurstDR spinal cord stimulation rebalances pain input and pain suppression in the brain in chronic neuropathic pain

ObjectiveChronic pain is processed by at least three well-known pathways, two pain provoking pathways including a medial ‘suffering’ and lateral ‘painfulness’ pathway. A third descending pain pathway modulates pain but is predominantly inhibitory. Chronic pain can be seen as an imbalance between the two pain-provoking and the pain inhibitory pathways. If this assumption is correct, then the imbalance between pain input and pain suppression should reverse and normalize in response to successful, i.e., pain reducing burstDR spinal cord stimulation, one of the current treatment options for neuropathic pain. Materials and methodsFifteen patients, who received spinal cord stimulation for failed back surgery were included in this study, using source localized electrical brain activity and connectivity recording via EEG to identify the purported imbalance. ResultsBurstDR spinal cord stimulation induces a significant change in EEG activity in both the left and right somatosensory cortex (SSC) for both θ and γ oscillations. In the dorsal anterior cingulate cortex (dACC), we observed a significant drop in both α and β oscillations. This reduction is accompanied by a change in pain intensity and suffering. BurstDR spinal cord stimulation is also associated with a reduction in θ at the pregenual anterior cingulate cortex (pgACC). Analyzing effective connectivity indicates that for the θ band, more information is sent from the pgACC to the left and right SSC. For α, increased information is sent from the pgACC to the dACC and both the left and right SSC. This is associated with a reduced θ-γ coupling in the SSC and reduced α-β coupling in dACC. ConclusionThis study suggests that chronic pain is indeed an imbalance between the ascending and descending pathways in the brain and that burst spinal cord stimulation can normalize this imbalance in the brain.