High-density EEG Research Articles

Are High Frequency Oscillations in Scalp EEG Related to Age?

BackgroundHigh-frequency oscillations (HFOs) have received much attention in recent years, particularly in the clinical context. In addition to their application as a marker for pathological changes in patients with epilepsy, HFOs have also been brought into context with several physiological mechanisms. Furthermore, recent studies reported a relation between an increase of HFO rate and age in invasive EEG recordings. The present study aimed to investigate whether this relation can be replicated in scalp-EEG.MethodsWe recorded high-density EEG from 11 epilepsy patients at rest as well as during motor performance. Manual detection of HFOs was performed by two independent raters following a standardized protocol. Patients were grouped by age into younger (<25 years) and older (>50 years) participants.ResultsNo significant difference of HFO-rates was found between groups [U = 10.5, p = 0.429, r = 0.3].ConclusionsLack of replicability of the age effect of HFOs may be due to the local propagation patterns of age-related HFOs occurring in deep structures. However, limitations such as small sample size, decreased signal-to-noise ratio as compared to invasive recordings, as well as HFO-mimicking artifacts must be considered.

Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes

We describe the multimodal neuroimaging dataset VEPCON (OpenNeuro Dataset ds003505). It includes raw data and derivatives of high-density EEG, structural MRI, diffusion weighted images (DWI) and single-trial behavior (accuracy, reaction time). Visual evoked potentials (VEPs) were recorded while participants (n = 20) discriminated briefly presented faces from scrambled faces, or coherently moving stimuli from incoherent ones. EEG and MRI were recorded separately from the same participants. The dataset contains raw EEG and behavioral data, pre-processed EEG of single trials in each condition, structural MRIs, individual brain parcellations at 5 spatial resolutions (83 to 1015 regions), and the corresponding structural connectomes computed from fiber count, fiber density, average fractional anisotropy and mean diffusivity maps. For source imaging, VEPCON provides EEG inverse solutions based on individual anatomy, with Python and Matlab scripts to derive activity time-series in each brain region, for each parcellation level. The BIDS-compatible dataset can contribute to multimodal methods development, studying structure-function relations, and to unimodal optimization of source imaging and graph analyses, among many other possibilities.

Anxiety in Parkinson's disease: A resting-state high density EEG study

ObjectiveTo identify markers of Parkinson's disease (PD) related anxiety, using high density electroencephalography (hd-EEG). Methods108 patients participated in the study. They were divided into two groups: with and without clinically relevant anxiety, according to their score on the Parkinson Anxiety Scale. Resting-state hd-EEG was recorded. Spectral and functional connectivity characteristics were compared between the two groups. ResultsThirty-three patients (31%) had significant anxiety symptoms. In the spectral analysis, relative power in the alpha1 frequency band in the right prefrontal cortex was lower in patients with anxiety than without. Functional connectivity analysis showed a stronger connectivity between the left insula and several regions of the right prefrontal cortex in patients with anxiety than in those without. ConclusionThis study shows the pivotal role of the insula and frontal cortex in the pathophysiology of anxiety in PD and extends the results of previous studies using magnetic resonance imaging or positron emission tomography imaging.

The Study of Face Processing in Social Anxiety Disorder Based on Face-Specific N170 Component.

Although previous studies showed that social anxiety disorder (SAD) exhibits the attentional bias for angry faces, few studies investigated effective face recognition combined with event-related potential (ERP) technique in SAD patients, especially the treatment effect. This study examines the differences in face processing in SAD patients before and after treatment and healthy control people (H-group). High-density EEG scans were registered in response to emotional schematic faces, particularly interested in the face processing N170 component. Analysis of N170 amplitude revealed a larger N170 for P-group-pre in response to inverted and upright stimuli than H-group in the right hemisphere. The result of the intragroup t-test showed that N170 was delayed for inverted relative to upright faces only in P-group-post and H-group but not in P-group-pre. Remarkably, the results of ANOVAs manifested that emotional expression cannot modulate N170 for SAD patients. Besides, the N170-based asymmetry index (AI) was introduced to analyze the left- and right-hemisphere dominance of N170 for three groups. It was found that, with the improvement of patients' treatment, the value of AIN170−base d presented a decreasing trend. These results together suggested that there was no inversion effect observed for patients with SAD. The change in the value of AIN170−base d can be used as potential electrophysiological markers for the diagnosis and treatment effects on patients with SAD.

Unsupervised learning of brain state dynamics during emotion imagination using high-density EEG

This study applies adaptive mixture independent component analysis (AMICA) to learn a set of ICA models, each optimized by fitting a distributional model for each identified component process while maximizing component process independence within some subsets of time points of a multi-channel EEG dataset. Here, we applied 20-model AMICA decomposition to long-duration (1–2 h), high-density (128-channel) EEG data recorded while participants used guided imagination to imagine situations stimulating the experience of 15 specified emotions. These decompositions tended to return models identifying spatiotemporal EEG patterns or states within single emotion imagination periods. Model probability transitions reflected time-courses of EEG dynamics during emotion imagination, which varied across emotions. Transitions between models accounting for imagined “grief” and “happiness” were more abrupt and better aligned with participant reports, while transitions for imagined “contentment” extended into adjoining “relaxation” periods. The spatial distributions of brain-localizable independent component processes (ICs) were more similar within participants (across emotions) than emotions (across participants). Across participants, brain regions with differences in IC spatial distributions (i.e., dipole density) between emotion imagination versus relaxation were identified in or near the left rostrolateral prefrontal, posterior cingulate cortex, right insula, bilateral sensorimotor, premotor, and associative visual cortex. No difference in dipole density was found between positive versus negative emotions. AMICA models of changes in high-density EEG dynamics may allow data-driven insights into brain dynamics during emotional experience, possibly enabling the improved performance of EEG-based emotion decoding and advancing our understanding of emotion.

Resting State Functional Connectivity Analysis During General Anesthesia: A High-Density EEG Study

The depth of anesthesia monitoring is helpful to guide administrations of general anesthetics during surgical procedures,however, the conventional 2-4 channels electroencephalogram (EEG) derived monitors have their limitations in monitoring conscious states due to low spatial resolution and suboptimal algorithm. In this study, 256-channel high-density EEG signals in 24 subjects receiving three types of general anesthetics (propofol, sevoflurane and ketamine) respectively were recorded both before and after anesthesia. The raw EEG signals were preprocessed by EEGLAB 14.0. Functional connectivity (FC) analysis by traditional coherence analysis (CA) method and a novel sparse representation (SR) method. And the network parameters, average clustering coefficient (ACC) and average shortest path length (ASPL) before and after anesthesia were calculated. The results show SR method find more significant FC differences in frontal and occipital cortices, and whole brain network (p<0.05). In contrast, CA can hardly obtain consistent ASPL in the whole brain network (p>0.05). Further, ASPL calculated by SR for whole brain connections in all of three anesthesia groups increased, which can be a unified EEG biomarker of general anesthetics-induced loss of consciousness (LOC). Therefore FC analysis based on SR analysis has better performance in distinguishing anesthetic-induced LOC from awake state.

Различия модуляции ответов мозга на эмоциональные стимулы у мужчин и женщин при биполярной депрессии

Study Objective: To investigate the prognostic and therapeutically significant characteristics in neurophysiological reactivity of male and female patients with bipolar depression. Study Design: comparative controlled non-randomized clinical experimental study. Materials and Methods. 53 patients (26 men and 27 women) with bipolar depression were examined before the start of their psychopharmacotherapy. They were aged from 21 to 59 years. There were no significant differences between men and women on the Hamilton Depression and Anxiety Scale. The groups of healthy volunteers included 23 men and 29 women of the corresponding age. The participants sorted the photos, 80 of which were images of angry/aggressive people or animals, and 80 had neutral expressions. Simple figures (keys) were displayed 2 seconds before the pictures, their connection with the photographs was not explained. A 128-channel electroencephalogram was recorded and the brain responses elicited by the keys were analyzed. Differences (p &lt; 0.05) between neutral and emotional conditions were defined as emotional modulation (EM). Study Results. EM differences in male and female patients were more pronounced than in the control groups. In women with bipolar depression, EM was consistently located in the posterior areas of the cortex from 100 ms to the end of the analysis period. The EM for the P100 component was missing, and the EM topography for P200 was closer to normal than in men. The EM of components N170 and P380 in patients differed depending on their sex and differed from EM of healthy controls of the same sex. Conclusions. The EM of the brain activity in patients with bipolar depression differs from the EM of healthy people. The differences depend on the gender of the patient and affect several components of the evoked brain activity. This suggests that it is important to take into account the gender of the subjects when studying affective disorders in patients. Keywords: emotional modulation, high-density EEG, bipolar depression, implicit learning, facial expression, anger.

Methodological considerations when measuring and analyzing auditory steady-state responses with multi-channel EEG.

The auditory steady-state response (ASSR) has been traditionally recorded with few electrodes and is often measured as the voltage difference between mastoid and vertex electrodes (vertical montage). As high-density EEG recording systems have gained popularity, multi-channel analysis methods have been developed to integrate the ASSR signal across channels. The phases of ASSR across electrodes can be affected by factors including the stimulus modulation rate and re-referencing strategy, which will in turn affect the estimated ASSR strength. To explore the relationship between the classical vertical-montage ASSR and whole-scalp ASSR, we applied these two techniques to the same data to estimate the strength of ASSRs evoked by tones with sinusoidal amplitude modulation rates of around 40, 100, and 200Hz. The whole-scalp methods evaluated in our study, with either linked-mastoid or common-average reference, included ones that assume equal phase across all channels, as well as ones that allow for different phase relationships. The performance of simple averaging was compared to that of more complex methods involving principal component analysis. Overall, the root-mean-square of the phase locking values (PLVs) across all channels provided the most efficient method to detect ASSR across the range of modulation rates tested here.

Accuracy of high-density EEG electrode position measurement using an optical scanner compared with the photogrammetry method

ObjectiveTo determine the feasibility and accuracy of a handheld optical scanner to measure the three-dimensional (3D) EEG electrode coordinates in a high-density array of 256 electrodes. MethodsWe compared the optical scanning with a previously validated method, based on photogrammetry. Electrode coordinates were co-registered with the MRI of the patients, and mean distance error relative to the three-dimensional MRI reconstruction was determined for each patient. We included 60 patients: 30 were measured using the photogrammetry method, and 30 age and gender matched patients were measured with the optical scanner. ResultsUsing the optical scanner, the mean distance error was 1.78 mm (95% confidence interval: 1.59–1.98 mm) which was significantly lower (p < 0.001) compared with the photogrammetry method (mean distance error: 2.43 mm; 95% confidence interval: 2.28–2.57 mm). The real-time scanning took 5–10 min per patient. ConclusionsThe handheld optical scanner is more accurate and feasible, compared to the photogrammetry method. SignificanceMeasuring EEG electrode positions in high-density array, using the optical scanner is suitable for clinical implementation in EEG source imaging for presurgical evaluation.

Performance Variation of a Somatosensory BCI Based on Imagined Sensation: A Large Population Study.

A proportion of users cannot achieve adequate brain-computer interface (BCI) control. The diversity of BCI modalities provides a way to solve this emerging issue. Here, we investigate the accuracy of a somatosensory BCI based on sensory imagery (SI). During the SI tasks, subjects were instructed to imagine a tactile sensation and to maintain the attention on the corresponding hand, as if there was tactile stimulus on the skin of the wrist. The performance across 106 healthy subjects in left- and right-hand SI discrimination was 78.9±13.2%. In 70.7% of the subjects the performance was above 70%. The SI task induced a contralateral cortical activation, and high-density EEG source localization showed that the real tactile stimulation and imagined tactile stimulation shared similar cortical activations within the somatosensory cortex. The somatosensory BCI based on SI provides a new signal modality for independent BCI development. Moreover, a combination of SI and other BCI modalities, such as motor imagery, may provide new avenues for further improving BCI usage and applicability, especially in those subjects unable to attain adequate BCI control with conventional BCI modalities.

Source EEG reveals that Rolandic epilepsy is a regional epileptic encephalopathy

Rolandic epilepsy is the most common form of epileptic encephalopathy, characterized by sleep-potentiated inferior Rolandic epileptiform spikes, seizures, and cognitive deficits in school-age children that spontaneously resolve by adolescence. We recently identified a paucity of sleep spindles, physiological thalamocortical rhythms associated with sleep-dependent learning, in the Rolandic cortex during the active phase of this disease. Because spindles are generated in the thalamus and amplified through regional thalamocortical circuits, we hypothesized that: 1) deficits in spindle rate would involve but extend beyond the inferior Rolandic cortex in active epilepsy and 2) regional spindle deficits would better predict cognitive function than inferior Rolandic spindle deficits alone. To test these hypotheses, we obtained high-resolution MRI, high-density EEG recordings, and focused neuropsychological assessments in children with Rolandic epilepsy during active (n = 8, age 9–14.7 years, 3F) and resolved (seizure free for > 1 year, n = 10, age 10.3–16.7 years, 1F) stages of disease and age-matched controls (n = 8, age 8.9–14.5 years, 5F). Using a validated spindle detector applied to estimates of electrical source activity in 31 cortical regions, including the inferior Rolandic cortex, during stages 2 and 3 of non-rapid eye movement sleep, we compared spindle rates in each cortical region across groups. Among detected spindles, we compared spindle features (power, duration, coherence, bilateral synchrony) between groups. We then used regression models to examine the relationship between spindle rate and cognitive function (fine motor dexterity, phonological processing, attention, and intelligence, and a global measure of all functions). We found that spindle rate was reduced in the inferior Rolandic cortices in active but not resolved disease (active P = 0.007; resolved P = 0.2) compared to controls. Spindles in this region were less synchronous between hemispheres in the active group (P = 0.005; resolved P = 0.1) compared to controls; but there were no differences in spindle power, duration, or coherence between groups. Compared to controls, spindle rate in the active group was also reduced in the prefrontal, insular, superior temporal, and posterior parietal regions (i.e., “regional spindle rate”, P < 0.039 for all). Independent of group, regional spindle rate positively correlated with fine motor dexterity (P < 1e-3), attention (P = 0.02), intelligence (P = 0.04), and global cognitive performance (P < 1e-4). Compared to the inferior Rolandic spindle rate alone, models including regional spindle rate trended to improve prediction of global cognitive performance (P = 0.052), and markedly improved prediction of fine motor dexterity (P = 0.006). These results identify a spindle disruption in Rolandic epilepsy that extends beyond the epileptic cortex and a potential mechanistic explanation for the broad cognitive deficits that can be observed in this epileptic encephalopathy.

The hippocampus as the switchboard between perception and memory

Adaptive memory recall requires a rapid and flexible switch from external perceptual reminders to internal mnemonic representations. However, owing to the limited temporal or spatial resolution of brain imaging modalities used in isolation, the hippocampal-cortical dynamics supporting this process remain unknown. We thus employed an object-scene cued recall paradigm across two studies, including intracranial electroencephalography (iEEG) and high-density scalp EEG. First, a sustained increase in hippocampal high gamma power (55 to 110 Hz) emerged 500 ms after cue onset and distinguished successful vs. unsuccessful recall. This increase in gamma power for successful recall was followed by a decrease in hippocampal alpha power (8 to 12 Hz). Intriguingly, the hippocampal gamma power increase marked the moment at which extrahippocampal activation patterns shifted from perceptual cue toward mnemonic target representations. In parallel, source-localized EEG alpha power revealed that the recall signal progresses from hippocampus to posterior parietal cortex and then to medial prefrontal cortex. Together, these results identify the hippocampus as the switchboard between perception and memory and elucidate the ensuing hippocampal-cortical dynamics supporting the recall process.

The Bilingual Lexicon, Back and Forth: Electrophysiological Signatures of Translation Asymmetry

Mainstream theories of first and second language (L1, L2) processing in bilinguals are crucially informed by word translation research. A core finding is the translation asymmetry effect, typified by slower performance in forward translation (FT, from L1 into L2) than in backward translation (BT, from L2 into L1). Yet, few studies have explored its neural bases and none has employed (de)synchronization measures, precluding the integration of bilingual memory models with neural (de)coupling accounts of word processing. Here, 27 proficient Spanish-English bilinguals engaged in FT and BT of single words as we obtained high-density EEG recordings to perform cluster-based oscillatory and non-linear functional connectivity analyses. Relative to BT, FT yielded slower responses, higher frontal theta (4–7 Hz) power in an early window (0–300 ms), reduced centro-posterior lower-beta (14–20 Hz) and centro-frontal upper-beta (21–30 Hz) power in a later window (300–600 ms), and lower fronto-parietal connectivity below 10 Hz in the early window. Also, the greater the behavioral difference between FT and BT, the greater the power of the early theta cluster for FT over BT. These results reveal key (de)coupling dynamics underlying translation asymmetry, offering frequency-specific constraints for leading models of bilingual lexical processing.

A multi-step blind source separation approach for the attenuation of artifacts in mobile high-density electroencephalography data

Objective. Electroencephalography (EEG) is a widely used technique to address research questions about brain functioning, from controlled laboratorial conditions to naturalistic environments. However, EEG data are affected by biological (e.g. ocular, myogenic) and non-biological (e.g. movement-related) artifacts, which—depending on their extent—may limit the interpretability of the study results. Blind source separation (BSS) approaches have demonstrated to be particularly promising for the attenuation of artifacts in high-density EEG (hdEEG) data. Previous EEG artifact removal studies suggested that it may not be optimal to use the same BSS method for different kinds of artifacts. Approach. In this study, we developed a novel multi-step BSS approach to optimize the attenuation of ocular, movement-related and myogenic artifacts from hdEEG data. For validation purposes, we used hdEEG data collected in a group of healthy participants in standing, slow-walking and fast-walking conditions. During part of the experiment, a series of tone bursts were used to evoke auditory responses. We quantified event-related potentials (ERPs) using hdEEG signals collected during an auditory stimulation, as well as the event-related desynchronization (ERD) by contrasting hdEEG signals collected in walking and standing conditions, without auditory stimulation. We compared the results obtained in terms of auditory ERP and motor-related ERD using the proposed multi-step BSS approach, with respect to two classically used single-step BSS approaches. Main results. The use of our approach yielded the lowest residual noise in the hdEEG data, and permitted to retrieve stronger and more reliable modulations of neural activity than alternative solutions. Overall, our study confirmed that the performance of BSS-based artifact removal can be improved by using specific BSS methods and parameters for different kinds of artifacts. Significance. Our technological solution supports a wider use of hdEEG-based source imaging in movement and rehabilitation studies, and contributes to the further development of mobile brain/body imaging applications.

How Do You Feel the Rhythm: Dynamic Motor-Auditory Interactions Are Involved in the Imagination of Hierarchical Timing.

Predicting and organizing patterns of events is important for humans to survive in a dynamically changing world. The motor system has been proposed to be actively, and necessarily, engaged in not only the production but the perception of rhythm by organizing hierarchical timing that influences auditory responses. It is not yet well understood how the motor system interacts with the auditory system to perceive and maintain hierarchical structure in time. This study investigated the dynamic interaction between auditory and motor functional sources during the perception and imagination of musical meters. We pursued this using a novel method combining high-density EEG, EMG, and motion capture with independent component analysis to separate motor and auditory activity during meter imagery while robustly controlling against covert movement. We demonstrated that endogenous brain activity in both auditory and motor functional sources reflects the imagination of binary and ternary meters in the absence of corresponding acoustic cues or overt movement at the meter rate. We found clear evidence for hypothesized motor-to-auditory information flow at the beat rate in all conditions, suggesting a role for top-down influence of the motor system on auditory processing of beat-based rhythms, and reflecting an auditory-motor system with tight reciprocal informational coupling. These findings align with and further extend a set of motor hypotheses from beat perception to hierarchical meter imagination, adding supporting evidence to active engagement of the motor system in auditory processing, which may more broadly speak to the neural mechanisms of temporal processing in other human cognitive functions.SIGNIFICANCE STATEMENT Humans live in a world full of hierarchically structured temporal information, the accurate perception of which is essential for understanding speech and music. Music provides a window into the brain mechanisms of time perception, enabling us to examine how the brain groups musical beats into, for example a march or waltz. Using a novel paradigm combining measurement of electrical brain activity with data-driven analysis, this study directly investigates motor-auditory connectivity during meter imagination. Findings highlight the importance of the motor system in the active imagination of meter. This study sheds new light on a fundamental form of perception by demonstrating how auditory-motor interaction may support hierarchical timing processing, which may have clinical implications for speech and motor rehabilitation.

Functional connectivity using high density EEG shows competitive reliability and agreement across test/retest sessions

BackgroundElectrophysiological resting state functional connectivity using high density electroencephalography (hdEEG) is gaining momentum. The increased resolution offered by hdEEG, usually either 128 or 256 channels, permits source localization of EEG signals on the cortical surface. However, the number of methodological options for the acquisition and analysis of resting state hdEEG is extremely large. These include acquisition duration, eyes open/closed, channel density, source localization methods, and functional connectivity metric. New methodsWe undertake an extensive examination of the test-retest reliability and methodological agreement of all these options for regional measures of functional connectivity. ResultsPower envelope connectivity shows larger test-retest reliability than imaginary coherence across all bands. While channel density doesn’t strongly impact reliability or agreement, source localization methods produce systematically different functional connectivity, highlighting an important obstacle for replicating results in the literature. Most importantly, reliability and agreement often plateaus at or after 6 minutes of acquisition, well beyond the typical duration of 3 minutes. Finally, our study demonstrates that resting EEG can be as or more reliable than resting fMRI acquired in the same individuals. ConclusionsThe competitive reliability and agreement of power envelope connectivity greatly increases our confidence in measuring resting state connectivity using EEG and its capacity to find individual differences.

Resting-state EEG reveals four subphenotypes of amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis is a devastating disease characterized primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. Amyotrophic lateral sclerosis is both clinically and biologically heterogeneous. Subgrouping is currently undertaken using clinical parameters, such as site of symptom onset (bulbar or spinal), burden of disease (based on the modified El Escorial Research Criteria) and genomics in those with familial disease. However, with the exception of genomics, these subcategories do not take into account underlying disease pathobiology, and are not fully predictive of disease course or prognosis.Recently, we have shown that resting-state EEG can reliably and quantitatively capture abnormal patterns of motor and cognitive network disruption in amyotrophic lateral sclerosis. These network disruptions have been identified across multiple frequency bands, and using measures of neural activity (spectral power) and connectivity (comodulation of activity by amplitude envelope correlation and synchrony by imaginary coherence) on source-localized brain oscillations from high-density EEG. Using data-driven methods (similarity network fusion and spectral clustering), we have now undertaken a clustering analysis to identify disease subphenotypes and to determine whether different patterns of disruption are predictive of disease outcome.We show that amyotrophic lateral sclerosis patients (n = 95) can be subgrouped into four phenotypes with distinct neurophysiological profiles. These clusters are characterized by varying degrees of disruption in the somatomotor (α-band synchrony), frontotemporal (β-band neural activity and γl-band synchrony) and frontoparietal (γl-band comodulation) networks, which reliably correlate with distinct clinical profiles and different disease trajectories. Using an in-depth stability analysis, we show that these clusters are statistically reproducible and robust, remain stable after reassessment using a follow-up EEG session, and continue to predict the clinical trajectory and disease outcome.Our data demonstrate that novel phenotyping using neuroelectric signal analysis can distinguish disease subtypes based exclusively on different patterns of network disturbances. These patterns may reflect underlying disease neurobiology. The identification of amyotrophic lateral sclerosis subtypes based on profiles of differential impairment in neuronal networks has clear potential in future stratification for clinical trials. Advanced network profiling in amyotrophic lateral sclerosis can also underpin new therapeutic strategies that are based on principles of neurobiology and designed to modulate network disruption.

Visual Motion and Decision-Making in Dyslexia: Reduced Accumulation of Sensory Evidence and Related Neural Dynamics.

Children with and without dyslexia differ in their behavioral responses to visual information, particularly when required to pool dynamic signals over space and time. Importantly, multiple processes contribute to behavioral responses. Here we investigated which processing stages are affected in children with dyslexia when performing visual motion processing tasks, by combining two methods that are sensitive to the dynamic processes leading to responses. We used a diffusion model which decomposes response time and accuracy into distinct cognitive constructs, and high-density EEG. Fifty children with dyslexia (24 male) and 50 typically developing children (28 male) 6-14 years of age judged the direction of motion as quickly and accurately as possible in two global motion tasks (motion coherence and direction integration), which varied in their requirements for noise exclusion. Following our preregistered analyses, we fitted hierarchical Bayesian diffusion models to the data, blinded to group membership. Unblinding revealed reduced evidence accumulation in children with dyslexia compared with typical children for both tasks. Additionally, we identified a response-locked EEG component which was maximal over centro-parietal electrodes which indicated a neural correlate of reduced drift rate in dyslexia in the motion coherence task, thereby linking brain and behavior. We suggest that children with dyslexia tend to be slower to extract sensory evidence from global motion displays, regardless of whether noise exclusion is required, thus furthering our understanding of atypical perceptual decision-making processes in dyslexia.SIGNIFICANCE STATEMENT Reduced sensitivity to visual information has been reported in dyslexia, with a lively debate about whether these differences causally contribute to reading difficulties. In this large preregistered study with a blind modeling approach, we combine state-of-the art methods in both computational modeling and EEG analysis to pinpoint the stages of processing that are atypical in children with dyslexia in two visual motion tasks that vary in their requirement for noise exclusion. We find reduced evidence accumulation in children with dyslexia across both tasks, and identify a neural marker, allowing us to link brain and behavior. We show that children with dyslexia exhibit general difficulties with extracting sensory evidence from global motion displays, not just in tasks that require noise exclusion.

Predictive Feedback, Early Sensory Representations, and Fast Responses to Predicted Stimuli Depend on NMDA Receptors.

Learned associations between stimuli allow us to model the world and make predictions, crucial for efficient behavior (e.g., hearing a siren, we expect to see an ambulance and quickly make way). While there are theoretical and computational frameworks for prediction, the circuit and receptor-level mechanisms are unclear. Using high-density EEG, Bayesian modeling, and machine learning, we show that inferred "causal" relationships between stimuli and frontal alpha activity account for reaction times (a proxy for predictions) on a trial-by-trial basis in an audiovisual delayed match-to-sample task which elicited predictions. Predictive β feedback activated sensory representations in advance of predicted stimuli. Low-dose ketamine, an NMDAR blocker, but not the control drug dexmedetomidine, perturbed behavioral indices of predictions, their representation in higher-order cortex, feedback to posterior cortex, and pre-activation of sensory templates in higher-order sensory cortex. This study suggests that predictions depend on alpha activity in higher-order cortex, β feedback, and NMDARs, and ketamine blocks access to learned predictive information.SIGNIFICANCE STATEMENT We learn the statistical regularities around us, creating associations between sensory stimuli. These associations can be exploited by generating predictions, which enable fast and efficient behavior. When predictions are perturbed, it can negatively influence perception and even contribute to psychiatric disorders, such as schizophrenia. Here we show that the frontal lobe generates predictions and sends them to posterior brain areas, to activate representations of predicted sensory stimuli before their appearance. Oscillations in neural activity (α and β waves) are vital for these predictive mechanisms. The drug ketamine blocks predictions and the underlying mechanisms. This suggests that the generation of predictions in the frontal lobe, and the feedback pre-activating sensory representations in advance of stimuli, depend on NMDARs.

P.128 Quantitative EEG Detects REM Sleep Microstructure

Background: Rapid eye movement sleep (REM) is divided into phasic and tonic microstates. Phasic REM is defined by presence of REMs with reportedly greater antiepileptic effect. We assessed whether quantitative EEG (QEEG) software can detect REM microstates. Methods: We applied artifact reduction and detection trends from QEEG software (Persyst 14) on 18 patients undergoing 30 day-night high density EEG recordings in the epilepsy monitoring unit. We identified phasic REM as 10-second epochs of previously human-scored REM that demonstrated presence of either vertical or horizontal eye movements on the QEEG artifact detection panel. Remaining epochs were identified as tonic REM. Results: Out of 91.2 average minutes of REM (range 24.5-167.5) per recording, a mean of 2.5% (range 0-18.9%) demonstrated eye movements intensive enough for QEEG artifact detection to be identified as phasic REM. On average, only 40% (range 0-500%) of eye movements per recording was flagged as vertical. Conclusions: These findings provide proof-of-concept that QEEG can automatically assess REM microstructure by readily detecting phasic and tonic REM. These findings also confirm that most REMs are horizontal. Having the ability to easily and automatically detect phasic versus tonic REM can help further future studies examining the antiepileptic effect of REM sleep.