Neuronal mechanisms of cortical alpha oscillations in awake-behaving macaques

EEG microstate biomarkers for schizophrenia: a novel approach using deep neural networks

Schizophrenia remains a challenging neuropsychiatric disorder with complex diagnostic processes. Current clinical approaches often rely on subjective assessments, highlighting the critical need for objective, quantitative diagnostic methods. This study aimed to develop a robust classification approach for schizophrenia using EEG microstate analysis and advanced machine learning techniques. We analyzed EEG signals from 14 healthy individuals and 14 patients with schizophrenia during a 15-min resting-state session across 19 EEG channels. A data augmentation strategy expanded the dataset to 56 subjects in each group. The signals were preprocessed and segmented into five frequency bands (delta, theta, alpha, beta, gamma) and five microstates (A, B, C, D, E) using k-means clustering. Five key features were extracted from each microstate: duration, occurrence, standard deviation, coverage, and frequency. A Deep Neural Network (DNN) model, along with other machine learning classifiers, was developed to classify the data. A comprehensive fivefold cross-validation approach evaluated model performance across various EEG channels, frequency bands, and feature combinations. Significant alterations in microstate transition probabilities were observed, particularly in higher frequency bands. The gamma band showed the most pronounced differences, with a notable disruption in D → A transitions (absolute difference = 0.100). The Random Forest classifier achieved the highest accuracy of 99.94% ± 0.12%, utilizing theta band features from the F8 frontal channel. The deep neural network model demonstrated robust performance with 98.31% ± 0.68% accuracy, primarily in the occipital region. Feature size 2 consistently provided optimal classification across most models. Our study introduces a novel, high-precision EEG microstate analysis approach for schizophrenia diagnosis, offering an objective diagnostic tool with potential applications in neuropsychiatric disorders. The findings reveal critical insights into neural dynamics associated with schizophrenia, demonstrating the potential for transforming clinical diagnostic practices through advanced machine learning and neurophysiological feature extraction.

Aging amplifies sex differences in low alpha and low beta EEG oscillations

Biological sex profoundly shapes brain function, yet its precise influence on neural oscillations was poorly understood. Despite decades of research, studies investigating sex-based variations in electroencephalographic (EEG) signals have yielded inconsistent findings that obstructs what may be a potentially crucial source of inter-individual variability in brain function. To address this, we analyzed five publicly available resting-state datasets, comprising EEG data (n = 445) and iEEG data (n = 103). Three age ranges were defined, young adult (YA, 18-30 years), middle-aged adult (MA, 30-55 years) and older adult (OA, 55-80 years). Our results revealed striking age-dependent sex differences: OA group exhibited robust sex differences, with males showing heightened low alpha (8-9 Hz) activity in temporal regions and attenuated low beta (16-20 Hz) oscillations in parietal-occipital areas compared to females. Intriguingly, these sex-specific patterns were absent in YA group, suggesting a complex interplay between sex and aging in shaping brain dynamics. The MA groups fall in between YA and OA group. The increase of low beta band activity in older female adults is strongly associated with hip size and BMI. Furthermore, we identified consistent sex-related activity in the precentral gyrus with the results of scalp EEG, potentially driving the observed scalp EEG differences. This multi-level analysis allowed us to bridge the gap between cortical and scalp-level observations, providing a more comprehensive picture of sex-related neural dynamics. The distinct associations between sex-specific oscillatory patterns and several lifestyle factors demonstrates the complex interplay between sex, age, and neural oscillations, revealing the variability in brain dynamics. Our findings highlight the importance of careful demographic consideration in EEG research design to ensure fairness in capturing the full spectrum of neurophysiological diversity.

Spoken words affect visual object recognition via the modulation of alpha and beta oscillations

Hearing spoken words can enhance the recognition of visual object categories. Yet, the mechanisms that underpin this facilitation are incompletely understood. Recent proposals suggest that words can alter visual processes by activating category-specific representations in sensory regions. Here, we tested the hypothesis that neural oscillations serve as a mechanism to activate language-generated visual representations. Participants performed a cue-picture matching task where cues were either spoken words, in their native or second language, or natural sounds, while their EEG and reaction times were recorded. Behaviorally, we found that images cued by words were recognized faster than those cued by natural sounds. This indicates that language activates more accurate semantic representations compared to natural sounds. A time-frequency analysis of cue-target intervals revealed that this label-advantage effect was associated with enhanced power in posterior alpha (9-11 Hz) and beta oscillations (17-19 Hz), both of which were larger when the image was preceded by a word compared to a natural sound. These results suggest that alpha and beta rhythms may play distinct functional roles to support language-mediated visual object recognition: alpha might function to amplify sensory representations in posterior regions, while beta may (re)activate the network states elicited by the auditory cue.

The role of alpha oscillations in resisting distraction

The role of alpha oscillations (8-13 Hz) in suppressing distractors is extensively debated. One debate concerns whether alpha oscillations suppress anticipated visual distractors through increased power. Whereas some studies suggest that alpha oscillations support distractor suppression, others do not. We identify methodological differences that may explain these discrepancies. A second debate concerns the mechanistic role of alpha oscillations. We and others previously proposed that alpha oscillations implement gain reduction in early visual regions when target load or distractor interference is high. Here, we suggest that parietal alpha oscillations support gating or stabilization of attentional focus and that alpha oscillations in ventral attention network (VAN) support resistance to attention capture. We outline future studies needed to uncover the precise mechanistic role of alpha oscillations.

Precise Spatial Tuning of Visually Driven Alpha Oscillations in Human Visual Cortex

Neuronal oscillations at about 10 Hz, called alpha oscillations, are often thought to arise from synchronous activity across occipital cortex, reflecting general cognitive states such as arousal and alertness. However, there is also evidence that modulation of alpha oscillations in visual cortex can be spatially specific. Here, we used intracranial electrodes in human patients to measure alpha oscillations in response to visual stimuli whose location varied systematically across the visual field. We separated the alpha oscillatory power from broadband power changes. The variation in alpha oscillatory power with stimulus position was then fit by a population receptive field (pRF) model. We find that the alpha pRFs have similar center locations to pRFs estimated from broadband power (70-180 Hz) but are several times larger. The results demonstrate that alpha suppression in human visual cortex can be precisely tuned. Finally, we show how the pattern of alpha responses can explain several features of exogenous visual attention.

Significance Statement

The alpha oscillation is the largest electrical signal generated by the human brain. An important question in systems neuroscience is the degree to which this oscillation reflects system-wide states and behaviors such as arousal, alertness, and attention, versus much more specific functions in the routing and processing of information. We examined alpha oscillations at high spatial precision in human patients with intracranial electrodes implanted over visual cortex. We discovered a surprisingly high spatial specificity of visually driven alpha oscillations, which we quantified with receptive field models. We further use our discoveries about properties of the alpha response to show a link between these oscillations and the spread of visual attention.Grant support: NIH R01 MH111417 (Petridou, Winawer, Ramsey, Devinsky); JSPS Overseas Research Fellowship (Yuasa)The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Monitoring Sleep Quality Through Low α-Band Activity in the Prefrontal Cortex Using a Portable Electroencephalogram Device: Longitudinal Study

BACKGROUND

The pursuit of sleep quality has become an important aspect of people's global quest for overall health. However, the objective neurobiological features corresponding to subjective perceptions of sleep quality remain poorly understood. Although previous studies have investigated the relationship between electroencephalogram (EEG) and sleep, the lack of longitudinal follow-up studies raises doubts about the reproducibility of their findings.

OBJECTIVE

Currently, there is a gap in research regarding the stable associations between EEG data and sleep quality assessed through multiple data collection sessions, which could help identify potential neurobiological targets related to sleep quality.

METHODS

In this study, we used a portable EEG device to collect resting-state prefrontal cortex EEG data over a 3-month follow-up period from 42 participants (27 in the first month, 25 in the second month, and 40 in the third month). Each month, participants' sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) to estimate their recent sleep quality.

RESULTS

We found that there is a significant and consistent positive correlation between low α band activity in the prefrontal cortex and PSQI scores (r=0.45, P<.001). More importantly, this correlation remained consistent across all 3-month follow-up recordings (P<.05), regardless of whether we considered the same cohort or expanded the sample size. Furthermore, we discovered that the periodic component of the low α band primarily contributed to this significant association with PSQI.

CONCLUSIONS

These findings represent the first identification of a stable and reliable neurobiological target related to sleep quality through multiple follow-up sessions. Our results provide a solid foundation for future applications of portable EEG devices in monitoring sleep quality and screening for sleep disorders in a broad population.

Neural Oscillations in the Somatosensory and Motor Cortex Distinguish Dexmedetomidine-Induced Anesthesia and Sleep in Rats

BACKGROUND

Anesthesia is featured by behavioral and physiological characteristics such as decreased sensory and motor function, loss of consciousness, etc. Some anesthetics such as dexmedetomidine (DEX), induce electroencephalogram signatures close to non-rapid eye movement sleep. Studies have shown that sleep is primarily driven by the activation of subcortical sleep-promoting neural pathways.

AIMS

However, the neuronal level electrophysiology features of anesthesia and how they differ from sleep is still not fully understood.

MATERIALS AND METHODS

In the present study, we recorded neuronal activity simultaneously from somatosensory cortex (S1) and motor cortex (M1) during awake, sleep, and DEX-induced anesthesia in rats.

RESULTS

The results show that DEX increased local field potential (LFP) power across a relatively wide band (1-25 Hz) in both S1 and M1. The coherence between S1 LFP and M1 LFP increased significantly in the delta and alpha bands. Power spectrum analysis during DEX-induced anesthesia revealed relatively high power in the delta and alpha bands, but low power in the theta and beta bands. Overall, the firing rate of individual neurons decreased after DEX. Correlation analysis of firing rate and LFP power indicate that more neurons were correlated, either positively or negatively, with LFPs during DEX-induced anesthesia compared to sleep.

DISCUSSION

Although these results showed enhancement of cortical LFP power in both DEX-induced anesthesia and sleep, different patterns of spike-field correlation suggest that the two states may be regulated by different cortical mechanisms.

CONCLUSION

Distinguishing anesthesia from sleep with neural oscillations could lead to more personalized, safer, and more effective approaches to managing consciousness in medical settings, with the potential for broad applications in neuroscience and clinical practice.

Large-scale interactions in predictive processing: oscillatory versus transient dynamics

How do the two main types of neural dynamics, aperiodic transients and oscillations, contribute to the interactions between feedforward (FF) and feedback (FB) pathways in sensory inference and predictive processing? We discuss three theoretical perspectives. First, we critically evaluate the theory that gamma and alpha/beta rhythms play a role in classic hierarchical predictive coding (HPC) by mediating FF and FB communication, respectively. Second, we outline an alternative functional model in which rapid sensory inference is mediated by aperiodic transients, whereas oscillations contribute to the stabilization of neural representations over time and plasticity processes. Third, we propose that the strong dependence of oscillations on predictability can be explained based on a biologically plausible alternative to classic HPC, namely dendritic HPC.

The effects of different challenge-level balance tasks on stroke cortical responses and balance assessment using EEG

Previous studies have validated that different balance tasks induce different cortical responses, which are key indexes of balance assessment. Assessing balance is crucial for stroke survivors to prevent falls and improve rehabilitation outcomes. However, it was unclear whether these tasks may affect the balance assessment, particularly regarding the relationship between task difficulty and the corresponding cortical responses involved in balance control. Therefore, we sought to explore the effects of different challenge-level balance tasks on balance assessment. Eighteen participants with stroke and thirteen healthy individuals were recruited in this study. The EEG was collected during sitting, standing and perturbation tasks. The pairwise-derived Brain Symmetry Index (pdBSI), and Granger Causality (GC) were analyzed with a two-way (task ×group) RMANOVA. Finally, a multiple linear regression analysis was applied to predict the BBS score with the above parameters. We found a significant interaction effect on pdBSI and GC. In the frontal lobe, participants with stroke exhibited significantly higher pdBSI (standing: p=0.042, perturbation: p=0.013) and lower GC (standing: p<0.001, perturbation: p=0.028) compared to healthy controls. Similarly, in the parietal lobe, stroke survivors showed markedly higher pdBSI (standing: p = 0.006, perturbation: p=0.012) and lower GC (standing: p=0.030, perturbation: p=0.011). Finally, The Berg Balance Scale (BBS) scores could be reliably predicted using parietal BSI and frontal GC metrics recorded during standing (p<0.001, adjusted R²=0.938) and perturbation tasks (p=0.001, adjusted R²=0.644). It was discovered that the more challenging balance tasks better revealed the difference in the power distribution and the directional functional connection between groups. The pdBSI and GC during standing and perturbation tasks, could be used as biomarkers for stroke balance assessment.

Interactive effects of social media use and puberty on resting-state cortical activity and mental health symptoms

Adolescence is a period of profound biopsychosocial development, with pubertally-driven neural reorganization as social demands increase in peer contexts. The explosive increase in social media access has fundamentally changed peer interactions among youth, creating an urgent need to understand its impact on neurobiological development and mental health. Extant literature indicates that using social media promotes social comparison and feedback seeking (SCFS) behaviors in youth, which portend increased risk for mental health disorders, but little is known about its impact on neurobiological development. We assessed social media behaviors, mental health symptoms, and spontaneous cortical activity using magnetoencephalography (MEG) in 80 typically developing youth (8-16 years) and tested how self-reported pubertal stage moderates their relationship. More mature adolescents who engaged in more SCFS showed weaker fusiform/parahippocampal alpha and medial prefrontal beta activity, and increased symptoms of anxiety and attention problems. Engaging in SCFS on social media during adolescence may thus relate to developmental differences in brain regions that undergo considerable development during puberty. These results are consistent with works indicating altered neurodevelopmental trajectories within association cortices surrounding the onset of many mental health disorders. Importantly, later pubertal stages may be most sensitive to the detrimental effects of social media use.

Increased alpha power in autistic adults: Relation to sensory behaviors and cortical volume

Alpha-band (~10 Hz) neural oscillations, crucial for gating sensory information, may offer insights into the atypical sensory experiences characteristic of autism spectrum disorder (ASD). We investigated alpha-band EEG activity in autistic adults (n = 29) compared with a nonautistic group (n = 23) under various stimulus-driven and resting-state conditions. The autistic group showed consistently higher alpha amplitude across all time points. In addition, there was proportionally more suppression of alpha at stimulus onset in the autistic group, and alpha amplitude in this stimulus-onset period correlated with sensory behaviors. Recent research suggests a link between subcortical structures' volume and cortical alpha magnitude. Prompted by this, we explored the association between alpha power and the volume of subcortical structures and total cortical volume in ASD. Our findings indicate a significant correlation with total cortical volume and a group by hippocampal volume interaction, pointing to the potential role of anatomical structural characteristics as potential modulators of cortical alpha oscillations in ASD. Overall, the results highlight altered alpha in autistic individuals as potentially contributing to the heightened sensory symptoms in autistic compared with nonautistic adults.

Strength of Low-Frequency EEG Phase Entrainment to External Stimuli Is Associated with Fluctuations in the Brain's Internal State

The brain attends to environmental rhythms by aligning the phase of internal oscillations. However, the factors underlying fluctuations in the strength of this phase entrainment remain largely unknown. In the present study, we examined whether the strength of low-frequency electroencephalography (EEG) phase entrainment to rhythmic stimulus sequences varied with the pupil size and posterior alpha-band power, thought to reflect the arousal level and excitability of posterior cortical brain areas, respectively. We recorded the pupil size and scalp EEG while participants carried out an intermodal selective attention task, in which they were instructed to attend to a rhythmic sequence of visual or auditory stimuli and ignore the other perceptual modality. As expected, intertrial phase coherence (ITC), a measure of entrainment strength, was larger for the task-relevant than for the task-irrelevant modality. Across the experiment, the pupil size and posterior alpha power were strongly linked with each other. Interestingly, ITC tracked both variables: larger pupil size was associated with a selective increase in entrainment to the task-relevant stimulus sequence, whereas larger posterior alpha power was associated with a decrease in phase entrainment to both the task-relevant and task-irrelevant stimulus sequences. Exploratory analyses showed that a temporal relation between ITC and posterior alpha power emerged in the time periods around pupil maxima and pupil minima. These results indicate that endogenous sources contribute distinctly to the fluctuations of EEG phase entrainment.

Increased visual alpha-band activity during self-paced finger tapping does not affect early visual stimulus processing

Alpha-band activity is thought to be involved in orchestrating neural processing within and across brain regions relevant to various functions such as perception, cognition, and motor activity. Across different studies, attenuated alpha-band activity has been linked to increased neural excitability. Yet, there have been conflicting results concerning the consequences of alpha-band modulations for early sensory processing. We here examined whether movement-related alterations in visual alpha-band activity affected the early sensory processing of visual stimuli. For this purpose, in an EEG experiment, participants were engaged in a voluntary finger-tapping task while passively viewing flickering dots. We found extensive and expected movement-related amplitude modulations of motor alpha- and beta-band activity with event-related-desynchronization (ERD) before and during, and event-related-synchronization (ERS) after single voluntary finger taps. Crucially, while a visual alpha-band ERS accompanied the motor alpha-ERD before and during each finger tap, flicker-evoked Steady-State-Visually-Evoked-Potentials (SSVEPs), as a marker of early visual sensory gain, were not modulated in amplitude. As early sensory stimulus processing was unaffected by amplitude-modulated visual alpha-band activity, this argues against the idea that alpha-band activity represents a mechanism by which early sensory gain modulation is implemented. The distinct neural dynamics of visual alpha-band activity and early sensory processing may point to distinct and multiplexed neural selection processes in visual processing.

Shared oscillatory mechanisms of alpha-band activity in prefrontal regions in eyes open and closed state using a portable EEG acquisition device

Alpha oscillations are associated with various psychiatric disorders, with many studies focusing on the prefrontal cortex, where transcranial alternating current stimulation (TACS) is applied in the alpha frequency band. This approach often involves selecting individualized alpha frequencies to resonate with their endogenous alpha oscillations. While strong alpha oscillations (8-13 Hz) are typically induced when the eyes are closed, they can also occur during the resting state with eyes open. However, it remains unclear whether these alpha oscillations share a common neural generation mechanism. Exploring which of these alpha oscillations is more suitable as a stable alpha peak frequency is a question of significant interest. Therefore, to systematically study this issue, we specifically collected resting-state electroencephalographic (EEG) data from the prefrontal region of 40 individuals, under both eyes-open and closed- eye conditions, with multiple follow-ups extending up to nine months. Through spectral analysis on each person's entire dataset and averaging the results, we observed a significant positive correlation between the alpha-band power in the eyes-open and the eyes-closed states, in terms of both absolute power and relative power. Further analysis revealed that this correlation was primarily contributed by the periodic activity within the alpha band. Upon modelling the oscillatory components, we discovered distinct differences in the oscillatory characteristics-such as number of the alpha sub-oscillations between the eyes-open state and the eyes-closed state. Our study is the first to systematically explored the relationship between alpha oscillations in the prefrontal cortex in the eyes-open and eyes-closed states, identifying both shared part of the neural generation mechanism and some distinct neural mechanisms that are unique to each state.

Intracranial EEG-Based Directed Functional Connectivity in Alpha to Gamma Frequency Range Reflects Local Circuits of the Human Mesiotemporal Network

To date, it is largely unknown how frequency range of neural oscillations measured with EEG is related to functional connectivity. To address this question, we investigated frequency-dependent directed functional connectivity among the structures of mesial and anterior temporal network including amygdala, hippocampus, temporal pole and parahippocampal gyrus in the living human brain. Intracranial EEG recording was obtained from 19 consecutive epilepsy patients with normal anterior mesial temporal MR imaging undergoing intracranial presurgical epilepsy diagnostics with multiple depth electrodes. We assessed intratemporal bidirectional functional connectivity using several causality measures such as Granger causality (GC), directed transfer function (DTF) and partial directed coherence (PDC) in a frequency-specific way. In order to verify the obtained results, we compared the spontaneous functional networks with intratemporal effective connectivity evaluated by means of SPES (single pulse electrical stimulation) method. The overlap with the evoked network was found for the functional connectivity assessed by the GC method, most prominent in the higher frequency bands (alpha, beta and low gamma), yet vanishing in the lower frequencies. Functional connectivity assessed by means of DTF and PCD obtained a similar directionality pattern with the exception of connectivity between hippocampus and parahippocampal gyrus which showed opposite directionality of predominant information flow. Whereas previous connectivity studies reported significant divergence between spontaneous and evoked networks, our data show the role of frequency bands for the consistency of functional and evoked intratemporal directed connectivity. This has implications for the suitability of functional connectivity methods in characterizing local brain circuits.

Is there a ubiquitous spectrolaminar motif of local field potential power across primate neocortex?

Mendoza-Halliday, Major et al., 2024 ("The Paper")1 advocates a local field potential (LFP)-based approach to functional identification of cortical layers during "laminar" (simultaneous recordings from all cortical layers) multielectrode recordings in nonhuman primates (NHPs). The Paper describes a "ubiquitous spectrolaminar motif" in the primate neocortex: 1) 75-150 Hz power peaks in the supragranular layers, 2) 10-19 Hz power peaks in the infragranular layers and 3) the crossing point of their laminar power gradients identifies Layer 4 (L4). Identification of L4 is critical in general, but especially for The Paper as the "motif" discovery is couched within a framework whose central hypothesis is that gamma activity originates in the supragranular layers and reflects feedforward activity, while alpha-beta activity originates in the infragranular layers and reflects feedback activity. In an impressive scientific effort, The Paper analyzed laminar data from 14 cortical areas in 2 prior macaque studies and compared them to marmoset, mouse, and human data to further bolster the canonical nature of the motif. Identification of such canonical principles of brain operation is clearly a topic of broad scientific interest. Similarly, a reliable online method for L4 identification would be of broad scientific value for the rapidly increasing use of laminar recordings using numerous evolving technologies. Despite The Paper's strengths, and its potential for scientific impact, a series of concerns that are fundamental to the analysis and interpretation of laminar activity profile data in general, and local field potential (LFP) signals in particular, led us to question its conclusions. We thus evaluated the generality of The Paper's methods and findings using new sets of data comprised of stimulus-evoked laminar response profiles from primary and higher-order auditory cortices (A1 and belt cortex), and primary visual cortex (V1). The rationale for using these areas as a test bed for new methods is that their laminar anatomy and physiology have already been extensively characterized by prior studies, and there is general agreement across laboratories on key matters like L4 identification. Our analyses indicate that The Paper's findings do not generalize well to any of these cortical areas. In particular, we find The Paper's methods for L4 identification to be unreliable. Moreover, both methodological and statistical concerns, outlined below and in the supplement, question the stated prevalence of the motif in The Paper's published dataset. After summarizing our findings and related broader concerns, we briefly critique the evidence from biophysical modeling studies cited to support The Paper's conclusions. While our findings are at odds with the proposition of a ubiquitous spectrolaminar motif in the primate neocortex, The Paper already has, and will continue to spark debate and further experimentation. Hopefully this countervailing presentation will lead to robust collegial efforts to define optimal strategies for applying laminar recording methods in future studies.

Experience Dependence of Alpha Rhythms and Neural Dynamics in the Mouse Visual Cortex

The role of experience in the development and maintenance of emergent network properties such as cortical oscillations and states is poorly understood. To define how early-life experience affects cortical dynamics in the visual cortex of adult, head-fixed mice, we examined the effects of two forms of blindness initiated before eye opening and continuing through recording: (1) bilateral loss of retinal input (enucleation) and (2) degradation of visual input (eyelid suture). Neither form of deprivation fundamentally altered the state-dependent regulation of firing rates or local field potentials. However, each deprivation caused unique changes in network behavior. Laminar analysis revealed two different generative mechanisms for low-frequency synchronization: one prevalent during movement and the other during quiet wakefulness. The former was absent in enucleated mice, suggesting a mouse homolog of human alpha oscillations. In addition, neurons in enucleated animals were less correlated and fired more regularly, but no change in mean firing rate. Eyelid suture decreased firing rates during quiet wakefulness, but not during movement, with no effect on neural correlations or regularity. Sutured animals showed a broadband increase in depth EEG power and an increased occurrence, but reduced central frequency, of narrowband gamma oscillations. The complementary-rather than additive-effects of lid suture and enucleation suggest that the development of emergent network properties does not require vision but is plastic to modified input. Our results suggest a complex interaction of internal set points and experience determines mature cortical activity, with low-frequency synchronization being particularly susceptible to early deprivation.

Oscillations in an artificial neural network convert competing inputs into a temporal code

The field of computer vision has long drawn inspiration from neuroscientific studies of the human and non-human primate visual system. The development of convolutional neural networks (CNNs), for example, was informed by the properties of simple and complex cells in early visual cortex. However, the computational relevance of oscillatory dynamics experimentally observed in the visual system are typically not considered in artificial neural networks (ANNs). Computational models of neocortical dynamics, on the other hand, rarely take inspiration from computer vision. Here, we combine methods from computational neuroscience and machine learning to implement multiplexing in a simple ANN using oscillatory dynamics. We first trained the network to classify individually presented letters. Post-training, we added temporal dynamics to the hidden layer, introducing refraction in the hidden units as well as pulsed inhibition mimicking neuronal alpha oscillations. Without these dynamics, the trained network correctly classified individual letters but produced a mixed output when presented with two letters simultaneously, indicating a bottleneck problem. When introducing refraction and oscillatory inhibition, the output nodes corresponding to the two stimuli activate sequentially, ordered along the phase of the inhibitory oscillations. Our model implements the idea that inhibitory oscillations segregate competing inputs in time. The results of our simulations pave the way for applications in deeper network architectures and more complicated machine learning problems.

Distinct oscillatory patterns differentiate between segregation and integration processes in perceptual grouping

Recently, there has been a resurgence in experimental and conceptual efforts to understand how brain rhythms can serve to organize visual information. Oscillations can provide temporal structure for neuronal processing and form a basis for integrating information across brain areas. Here, we use a bistable paradigm and a data-driven approach to test the hypothesis that oscillatory modulations associate with the integration or segregation of visual elements. Spectral signatures of perception of bound and unbound configurations of visual moving stimuli were studied using magnetoencephalography (MEG) in ambiguous and unambiguous conditions. Using a 2 × 2 design, we were able to isolate correlates from visual integration, either perceptual or stimulus-driven, from attentional and ambiguity-related activity. Two frequency bands were found to be modulated by visual integration: an alpha/beta frequency and a higher frequency gamma-band. Alpha/beta power was increased in several early visual cortical and dorsal visual areas during visual integration, while gamma-band power was surprisingly increased in the extrastriate visual cortex during segregation. This points to an integrative role for alpha/beta activity, likely from top-down signals maintaining a single visual representation. On the other hand, when more representations have to be processed in parallel gamma-band activity is increased, which is at odds with the notion that gamma oscillations are related to perceptual coherence. These modulations were confirmed in intracranial EEG recordings and partially originate from distinct brain areas. Our MEG and stereo-EEG data confirms predictions of binding mechanisms depending on low-frequency activity for long-range integration and for organizing visual processing while refuting a straightforward correlation between gamma-activity and perceptual binding. PRACTITIONER POINTS: Distinct neurophysiological signals underlie competing bistable percepts. Increased alpha/beta activity correlate with visual integration while gamma correlates with segmentation. Ambiguous percepts drive alpha/beta activity in the posterior cingulate cortex.

The neuromechanical of Beta-band corticomuscular coupling within the human motor system

Beta-band activity in the sensorimotor cortex is considered a potential biomarker for evaluating motor functions. The intricate connection between the brain and muscle (corticomuscular coherence), especially in beta band, was found to be modulated by multiple motor demands. This coherence also showed abnormality in motion-related disorders. However, although there has been a substantial accumulation of experimental evidence, the neural mechanisms underlie corticomuscular coupling in beta band are not yet fully clear, and some are still a matter of controversy. In this review, we summarized the findings on the impact of Beta-band corticomuscular coherence to multiple conditions (sports, exercise training, injury recovery, human functional restoration, neurodegenerative diseases, age-related changes, cognitive functions, pain and fatigue, and clinical applications), and pointed out several future directions for the scientific questions currently unsolved. In conclusion, an in-depth study of Beta-band corticomuscular coupling not only elucidates the neural mechanisms of motor control but also offers new insights and methodologies for the diagnosis and treatment of motor rehabilitation and related disorders. Understanding these mechanisms can lead to personalized neuromodulation strategies and real-time neurofeedback systems, optimizing interventions based on individual neurophysiological profiles. This personalized approach has the potential to significantly improve therapeutic outcomes and athletic performance by addressing the unique needs of each individual.

A Systematic Meta-Analysis of the Effectiveness of (Left) Hand Contractions on Motor Performance

Unilateral left hand contractions (LHCs) have been used as a neurophysiologically motivated intervention for optimizing and stabilizing a wide range of behaviors, including motor performance. What is currently unknown however is the efficacy and neurophysiological basis of LHC interventions. The aim of this systematic meta-analysis was to review and synthesize the evidence on the effectiveness of LHCs, as a pre-performance routine, on motor performance. We, therefore, discuss the theoretical background, highlights key methodological considerations, and suggest areas of future research. An electronic search of the PubMed, PsycInfo, Google Scholar, Scopus, and SPORTDiscus databases was conducted to identify peer-reviewed literature relating to LHCs and motor performance, skilled performance, peak performance, and choking. Ten studies (12 effect sizes) met inclusion criteria and were retained for quality assessment and synthesis. The findings indicate a moderate effect of LHC interventions on performance (g = 0.59, 95% CI = 0.14, 1.03); further, moderator analyses revealed that the effect was significant in experimental design studies, among sub-elite and elite athletes, and when participants were exposed to high-pressure situations. A trim-and-fill analysis was conducted to estimate and correct any potential publication bias, and the results suggest the effect of the LHC intervention may not be as robust as initially appeared. We conclude that this moderate effect should be interpreted with caution. Specifically, concerns regarding study quality and lack of neurophysiological specificity appear to limit the impact and significance of the LHC literature as it currently stands.

Attentional selection and communication through coherence: Scope and limitations

Synchronous neural oscillations are strongly associated with a variety of perceptual, cognitive, and behavioural processes. It has been proposed that the role of the synchronous oscillations in these processes is to facilitate information transmission between brain areas, the 'communication through coherence,' or CTC hypothesis. The details of how this mechanism would work, however, and its causal status, are still unclear. Here we investigate computationally a proposed mechanism for selective attention that directly implicates the CTC as causal. The mechanism involves alpha band (about 10 Hz) oscillations, originating in the pulvinar nucleus of the thalamus, being sent to communicating cortical areas, organizing gamma (about 40 Hz) oscillations there, and thus facilitating phase coherence and communication between them. This is proposed to happen contingent on control signals sent from higher-level cortical areas to the thalamic reticular nucleus, which controls the alpha oscillations sent to cortex by the pulvinar. We studied the scope of this mechanism in parameter space, and limitations implied by this scope, using a computational implementation of our conceptual model. Our results indicate that, although the CTC-based mechanism can account for some effects of top-down and bottom-up attentional selection, its limitations indicate that an alternative mechanism, in which oscillatory coherence is caused by communication between brain areas rather than being a causal factor for it, might operate in addition to, or even instead of, the CTC mechanism.

Variation and convergence in the morpho-functional properties of the mammalian neocortex

Man's natural inclination to classify and hierarchize the living world has prompted neurophysiologists to explore possible differences in brain organisation between mammals, with the aim of understanding the diversity of their behavioural repertoires. But what really distinguishes the human brain from that of a platypus, an opossum or a rodent? In this review, we compare the structural and electrical properties of neocortical neurons in the main mammalian radiations and examine their impact on the functioning of the networks they form. We discuss variations in overall brain size, number of neurons, length of their dendritic trees and density of spines, acknowledging their increase in humans as in most large-brained species. Our comparative analysis also highlights a remarkable consistency, particularly pronounced in marsupial and placental mammals, in the cell typology, intrinsic and synaptic electrical properties of pyramidal neuron subtypes, and in their organisation into functional circuits. These shared cellular and network characteristics contribute to the emergence of strikingly similar large-scale physiological and pathological brain dynamics across a wide range of species. These findings support the existence of a core set of neural principles and processes conserved throughout mammalian evolution, from which a number of species-specific adaptations appear, likely allowing distinct functional needs to be met in a variety of environmental contexts.

A closed-loop auditory stimulation approach selectively modulates alpha oscillations and sleep onset dynamics in humans

Alpha oscillations play a vital role in managing the brain's resources, inhibiting neural activity as a function of their phase and amplitude, and are changed in many brain disorders. Developing minimally invasive tools to modulate alpha activity and identifying the parameters that determine its response to exogenous modulators is essential for the implementation of focussed interventions. We introduce Alpha Closed-Loop Auditory Stimulation (αCLAS) as an EEG-based method to modulate and investigate these brain rhythms in humans with specificity and selectivity, using targeted auditory stimulation. Across a series of independent experiments, we demonstrate that αCLAS alters alpha power, frequency, and connectivity in a phase, amplitude, and topography-dependent manner. Using single-pulse-αCLAS, we show that the effects of auditory stimuli on alpha oscillations can be explained within the theoretical framework of oscillator theory and a phase-reset mechanism. Finally, we demonstrate the functional relevance of our approach by showing that αCLAS can interfere with sleep onset dynamics in a phase-dependent manner.

Exploring resting-state EEG oscillations in patients with Neuromyelitis Optica Spectrum Disorder

BACKGROUND AND OBJECTIVE

Quantitative resting-state electroencephalography (rs-EEG) is a convenient method for characterizing the functional impairments and adaptations of the brain that has been shown to be valuable for assessing many neurological and psychiatric disorders, especially in monitoring disease status and assisting neuromodulation treatment. However, it has not yet been explored in patients with neuromyelitis optica spectrum disorder (NMOSD). This study aimed to investigate the rs-EEG features of NMOSD patients and explore the rs-EEG features related to disease characteristics and complications (such as anxiety, depression, and fatigue).

METHODS

A total of 32 NMOSD patients and 20 healthy controls (HCs) were recruited; their demographic and disease information were collected, and their anxiety, depression, and fatigue symptoms were evaluated. The rs-EEG power spectra of all the participants were obtained. After excluding the participants with low-quality rs-EEG data during processing, statistical analysis was conducted based on the clinical information and rs-EEG data of 29 patients and 19 HCs. The rs-EEG power (the mean spectral energy (MSE) of absolute power and relative power in all frequency bands, as well as the specific power for all electrode sites) of NMOSD patients and HCs was compared. Furthermore, correlation analyses were performed between rs-EEG power and other variables for NMOSD patients (including the disease characteristics and complications).

RESULTS

The distribution of the rs-EEG power spectra in NMOSD patients was similar to that in HCs. The dominant alpha-peaks shifted significantly towards a lower frequency for patients when compared to HCs. The delta and theta power was significantly increased in the NMOSD group compared to that in the HC group. The alpha oscillation power was found to be significantly negatively associated with the degree of anxiety (reflected by the anxiety subscore of hospital anxiety and depression scale (HADS)) and the degree of depression (reflected by the depression subscore of HADS). The gamma oscillation power was revealed to be significantly positively correlated with the fatigue severity scale (FSS) score, while further analysis indicated that the electrode sites of almost the whole brain region showing correlations with fatigue. Regarding the disease variables, no statistically significant rs-EEG features were related to the main disease features in NMOSD patients.

CONCLUSION

The results of this study suggest that the rs-EEG power spectra of NMOSD patients show increased slow oscillations and are potential biomarkers of widespread white matter microstructural damage in NMOSD. Moreover, this study revealed the rs-EEG features associated with anxiety, depression, and fatigue in NMOSD patients, which might help in the evaluation of these complications and the development of neuromodulation treatment. Quantitative rs-EEG analysis may play an important role in the management of NMOSD patients, and future studies are warranted to more comprehensively understand its application value.

A ubiquitous spectrolaminar motif of local field potential power across the primate cortex

The mammalian cerebral cortex is anatomically organized into a six-layer motif. It is currently unknown whether a corresponding laminar motif of neuronal activity patterns exists across the cortex. Here we report such a motif in the power of local field potentials (LFPs). Using laminar probes, we recorded LFPs from 14 cortical areas across the cortical hierarchy in five macaque monkeys. The laminar locations of recordings were histologically identified by electrolytic lesions. Across all areas, we found a ubiquitous spectrolaminar pattern characterized by an increasing deep-to-superficial layer gradient of high-frequency power peaking in layers 2/3 and an increasing superficial-to-deep gradient of alpha-beta power peaking in layers 5/6. Laminar recordings from additional species showed that the spectrolaminar pattern is highly preserved among primates-macaque, marmoset and human-but more dissimilar in mouse. Our results suggest the existence of a canonical layer-based and frequency-based mechanism for cortical computation.

Alpha and SSVEP power outperform gamma power in capturing attentional modulation in human EEG

Attention typically reduces power in the alpha (8-12 Hz) band and increases power in gamma (>30 Hz) band in brain signals, as reported in macaque local field potential (LFP) and human electro/magneto-encephalogram (EEG/MEG) studies. In addition, EEG studies often use flickering stimuli that produce a specific measure called steady-state-visually-evoked-potential (SSVEP), whose power also increases with attention. However, effectiveness of these neural measures in capturing attentional modulation is unknown since stimuli and task paradigms vary widely across studies. In a recent macaque study, attentional modulation was more salient in the gamma band of the LFP, compared to alpha or SSVEP. To compare this with human EEG, we designed an orientation change detection task where we presented both static and counterphasing stimuli of matched difficulty levels to 26 subjects and compared attentional modulation of various measures under similar conditions. We report two main results. First, attentional modulation was comparable for SSVEP and alpha. Second, non-foveal stimuli produced weak gamma despite various stimulus optimizations and showed negligible attentional modulation although full-screen gratings showed robust gamma activity. Our results are useful for brain-machine-interfacing studies where suitable features are used for decoding attention, and also provide clues about spatial scales of neural mechanisms underlying attention.

Feedforward attentional selection in sensory cortex

Salient objects grab attention because they stand out from their surroundings. Whether this phenomenon is accomplished by bottom-up sensory processing or requires top-down guidance is debated. We tested these alternative hypotheses by measuring how early and in which cortical layer(s) neural spiking distinguished a target from a distractor. We measured synaptic and spiking activity across cortical columns in mid-level area V4 of male macaque monkeys performing visual search for a color singleton. A neural signature of attentional capture was observed in the earliest response in the input layer 4. The magnitude of this response predicted response time and accuracy. Errant behavior followed errant selection. Because this response preceded top-down influences and arose in the cortical layer not targeted by top-down connections, these findings demonstrate that feedforward activation of sensory cortex can underlie attentional priority.

The Influence of Prestimulus 1/f-Like versus Alpha-Band Activity on Subjective Awareness of Auditory and Visual Stimuli

Alpha rhythmic activity is often suggested to exert an inhibitory influence on information processing. However, relatively little is known about how reported alpha-related effects are influenced by a potential confounding element of the neural signal, power-law scaling. In the current study, we systematically examine the effect of accounting for 1/f activity on the relation between prestimulus alpha power and human behavior during both auditory and visual detection (N = 27; 19 female, 6 male, 2 nonbinary). The results suggest that, at least in the scalp-recorded EEG signal, the difference in alpha power often reported before visual hits versus misses is probably best thought of as a combination of narrowband alpha and broadband shifts. That is, changes in broadband parameters (exponent and offset of 1/f-like activity) also appear to be strong predictors of the subsequent awareness of visual stimuli. Neither changes in posterior alpha power nor changes in 1/f-like activity reliably predicted detection of auditory stimuli. These results appear consistent with suggestions that broadband changes in the scalp-recorded EEG signal may account for a portion of prior results linking alpha band dynamics to visuospatial attention and behavior, and suggest that systematic re-examination of existing data may be warranted.Significance Statement Fluctuations in alpha band (∼8-12 Hz) activity systematically follow the allocation of attention across space and sensory modality. Increases in alpha amplitude, which often precede failures to report awareness of threshold visual stimuli, are suggested to exert an inhibitory influence on information processing. However, fluctuations in alpha activity are often confounded with changes in the broadband 1/f-like pattern of the neural signal. When both factors are considered, we find that changes in broadband activity are as effective as narrowband alpha activity as predictors of subsequent visual detection. These results are consistent with emerging understanding of the potential functional importance of broadband changes in the neural signal and may have significant consequences for our understanding of alpha rhythmic activity.

Selection within working memory impairs perceptual detection

There is broad consensus supporting the reciprocal influence of working memory (WM) and attention. Top-down mechanisms operate to cope with either environmental or internal demands. In that sense, it is possible to select an item within the contents of WM to endow it with prioritized access. Although evidence supports that maintaining an item in this privileged state does not rely on sustained visual attention, it is unknown whether selection within WM depends on perceptual attention. To answer this question, we recorded electrophysiological neural activity while participants performed a retro-cue task in which we inserted a detection task in the delay period after retro-cue presentation. Critically, the onset of to-be-detected near threshold stimuli was unpredictable, and thus, sustained perceptual spatial attention was needed to accomplish the detection task from the offset of the retro-cue. At a behavioral level, we found decreased visual detection when a WM representation was retro-cued. At a neural level, alpha oscillatory activity confirmed a spatial shift of attention to the retro-cued representation. We interpret the convergence of neural oscillations and behavioral data to point towards the theory that selection within WM could be accomplished through a perceptual attentional mechanism.

Neural α Oscillations and Pupil Size Differentially Index Cognitive Demand under Competing Audiovisual Task Conditions

Cognitive demand is thought to modulate two often used, but rarely combined, measures: pupil size and neural α (8-12 Hz) oscillatory power. However, it is unclear whether these two measures capture cognitive demand in a similar way under complex audiovisual-task conditions. Here we recorded pupil size and neural α power (using electroencephalography), while human participants of both sexes concurrently performed a visual multiple object-tracking task and an auditory gap detection task. Difficulties of the two tasks were manipulated independent of each other. Participants' performance decreased in accuracy and speed with increasing cognitive demand. Pupil size increased with increasing difficulty for both the auditory and the visual task. In contrast, α power showed diverging neural dynamics: parietal α power decreased with increasing difficulty in the visual task, but not with increasing difficulty in the auditory task. Furthermore, independent of task difficulty, within-participant trial-by-trial fluctuations in pupil size were negatively correlated with α power. Difficulty-induced changes in pupil size and α power, however, did not correlate, which is consistent with their different cognitive-demand sensitivities. Overall, the current study demonstrates that the dynamics of the neurophysiological indices of cognitive demand and associated effort are multifaceted and potentially modality-dependent under complex audiovisual-task conditions.SIGNIFICANCE STATEMENT Pupil size and oscillatory α power are associated with cognitive demand and effort, but their relative sensitivity under complex audiovisual-task conditions is unclear, as is the extent to which they share underlying mechanisms. Using an audiovisual dual-task paradigm, we show that pupil size increases with increasing cognitive demands for both audition and vision. In contrast, changes in oscillatory α power depend on the respective task demands: parietal α power decreases with visual demand but not with auditory task demand. Hence, pupil size and α power show different sensitivity to cognitive demands, perhaps suggesting partly different underlying neural mechanisms.

Spike-phase coupling patterns reveal laminar identity in primate cortex

The cortical column is one of the fundamental computational circuits in the brain. In order to understand the role neurons in different layers of this circuit play in cortical function it is necessary to identify the boundaries that separate the laminar compartments. While histological approaches can reveal ground truth they are not a practical means of identifying cortical layers in vivo. The gold standard for identifying laminar compartments in electrophysiological recordings is current-source density (CSD) analysis. However, laminar CSD analysis requires averaging across reliably evoked responses that target the input layer in cortex, which may be difficult to generate in less well-studied cortical regions. Further, the analysis can be susceptible to noise on individual channels resulting in errors in assigning laminar boundaries. Here, we have analyzed linear array recordings in multiple cortical areas in both the common marmoset and the rhesus macaque. We describe a pattern of laminar spike-field phase relationships that reliably identifies the transition between input and deep layers in cortical recordings from multiple cortical areas in two different non-human primate species. This measure corresponds well to estimates of the location of the input layer using CSDs, but does not require averaging or specific evoked activity. Laminar identity can be estimated rapidly with as little as a minute of ongoing data and is invariant to many experimental parameters. This method may serve to validate CSD measurements that might otherwise be unreliable or to estimate laminar boundaries when other methods are not practical.

A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings

Cortical function emerges from the interactions of multi-scale networks that may be studied at a high level using neural mass models (NMM) that represent the mean activity of large numbers of neurons. Here, we provide first a new framework called laminar NMM, or LaNMM for short, where we combine conduction physics with NMMs to simulate electrophysiological measurements. Then, we employ this framework to infer the location of oscillatory generators from laminar-resolved data collected from the prefrontal cortex in the macaque monkey. We define a minimal model capable of generating coupled slow and fast oscillations, and we optimize LaNMM-specific parameters to fit multi-contact recordings. We rank the candidate models using an optimization function that evaluates the match between the functional connectivity (FC) of the model and data, where FC is defined by the covariance between bipolar voltage measurements at different cortical depths. The family of best solutions reproduces the FC of the observed electrophysiology by selecting locations of pyramidal cells and their synapses that result in the generation of fast activity at superficial layers and slow activity across most depths, in line with recent literature proposals. In closing, we discuss how this hybrid modeling framework can be more generally used to infer cortical circuitry.

Sleep/wake changes in perturbational complexity in rats and mice

In humans, the level of consciousness is assessed by quantifying the spatiotemporal complexity of cortical responses using Perturbational Complexity Index (PCI) and related PCIst (st, state transitions). Here we validate PCIst in freely moving rats and mice by showing that it is lower in NREM sleep and slow wave anesthesia than in wake or REM sleep, as in humans. We then show that (1) low PCIst is associated with the occurrence of an OFF period of neuronal silence; (2) stimulation of deep, but not superficial, cortical layers leads to reliable PCIst changes across sleep/wake and anesthesia; (3) consistent PCIst changes are independent of which single area is being stimulated or recorded, except for recordings in mouse prefrontal cortex. These experiments show that PCIst can reliably measure vigilance states in unresponsive animals and support the hypothesis that it is low when an OFF period disrupts causal interactions in cortical networks.

Neural signatures of task-related fluctuations in auditory attention and age-related changes

Listening in everyday life requires attention to be deployed dynamically - when listening is expected to be difficult and when relevant information is expected to occur - to conserve mental resources. Conserving mental resources may be particularly important for older adults who often experience difficulties understanding speech. In the current study, we use electro- and magnetoencephalography to investigate the neural and behavioral mechanics of attention regulation during listening and the effects that aging has on these. We first show in younger adults (17-31 years) that neural alpha oscillatory activity indicates when in time attention is deployed (Experiment 1) and that deployment depends on listening difficulty (Experiment 2). Experiment 3 investigated age-related changes in auditory attention regulation. Middle-aged and older adults (54-72 years) show successful attention regulation but appear to utilize timing information differently compared to younger adults (20-33 years). We show a notable age-group dissociation in recruited brain regions. In younger adults, superior parietal cortex underlies alpha power during attention regulation, whereas, in middle-aged and older adults, alpha power emerges from more ventro-lateral areas (posterior temporal cortex). This difference in the sources of alpha activity between age groups only occurred during task performance and was absent during rest (Experiment S1). In sum, our study suggests that middle-aged and older adults employ different neural control strategies compared to younger adults to regulate attention in time under listening challenges.

Relating neural oscillations to laminar fMRI connectivity in visual cortex

Laminar functional magnetic resonance imaging (fMRI) holds the potential to study connectivity at the laminar level in humans. Here we analyze simultaneously recorded electroencephalography (EEG) and high-resolution fMRI data to investigate how EEG power modulations, induced by a task with an attentional component, relate to changes in fMRI laminar connectivity between and within brain regions in visual cortex. Our results indicate that our task-induced decrease in beta power relates to an increase in deep-to-deep layer coupling between regions and to an increase in deep/middle-to-superficial layer connectivity within brain regions. The attention-related alpha power decrease predominantly relates to reduced connectivity between deep and superficial layers within brain regions, since, unlike beta power, alpha power was found to be positively correlated to connectivity. We observed no strong relation between laminar connectivity and gamma band oscillations. These results indicate that especially beta band, and to a lesser extent, alpha band oscillations relate to laminar-specific fMRI connectivity. The differential effects for alpha and beta bands indicate that they relate to different feedback-related neural processes that are differentially expressed in intra-region laminar fMRI-based connectivity.

Commonalities between the Berger Rhythm and spectra differences driven by cross-modal attention and imagination

When people close their eyes, the power of alpha-band oscillatory brain activity increases. We explored the possibility that this could be related to a suppression of visual processing, rather than being a default dynamic of the visual brain. We recorded brain activity while people meditated with their eyes open or closed, and when people attended to or imagined having auditory or visual experiences. We could decode the attended or imagined modality of experiences based on the spectra of brain activity that prevailed while meditating with open or closed eyes. We also found anecdotal evidence suggesting the strength of imagined sensory experiences may be predicted by the dynamics of neural networks that are responsive to inputs. Overall, our data suggest spectra changes when people close their eyes might relate to a targeted suppression of visual processing, as opposed to being a default state of idle visual brains.

Compensatory mechanism of attention-deficit/hyperactivity disorder recovery in resting state alpha rhythms

Alpha rhythms in the human electroencephalogram (EEG), oscillating at 8-13 Hz, are located in parieto-occipital cortex and are strongest when awake people close their eyes. It has been suggested that alpha rhythms were related to attention-related functions and mental disorders (e.g., Attention-deficit/hyperactivity disorder (ADHD)). However, many studies have shown inconsistent results on the difference in alpha oscillation between ADHD and control groups. Hence it is essential to verify this difference. In this study, a dataset of EEG recording (128 channel EGI) from 87 healthy controls (HC) and 162 ADHD (141 persisters and 21 remitters) adults in a resting state with their eyes closed was used to address this question and a three-gauss model (summation of baseline and alpha components) was conducted to fit the data. To our surprise, the power of alpha components was not a significant difference among the three groups. Instead, the baseline power of remission and HC group in the alpha band is significantly stronger than that of persister groups. Our results suggest that ADHD recovery may have compensatory mechanisms and many abnormalities in EEG may be due to the influence of behavior rather than the difference in brain signals.

Visual evoked feedforward-feedback traveling waves organize neural activity across the cortical hierarchy in mice

Sensory processing is distributed among many brain regions that interact via feedforward and feedback signaling. Neuronal oscillations have been shown to mediate intercortical feedforward and feedback interactions. Yet, the macroscopic structure of the multitude of such oscillations remains unclear. Here, we show that simple visual stimuli reliably evoke two traveling waves with spatial wavelengths that cover much of the cerebral hemisphere in awake mice. 30-50 Hz feedforward waves arise in primary visual cortex (V1) and propagate rostrally, while 3-6 Hz feedback waves originate in the association cortex and flow caudally. The phase of the feedback wave modulates the amplitude of the feedforward wave and synchronizes firing between V1 and parietal cortex. Altogether, these results provide direct experimental evidence that visual evoked traveling waves percolate through the cerebral cortex and coordinate neuronal activity across broadly distributed networks mediating visual processing.

Expectance of low contrast retro-cues does not modulate anticipatory alpha power

It has been proposed that alpha oscillations reflect the endogenous modulation of visual cortex excitability. In particular, alpha power increases during the maintenance period in Working Memory (WM) tasks have been interpreted as a mechanism to avoid potential interference of incoming stimuli. In this study we tested whether alpha power was modulated during the maintenance of WM to enhance the processing of relevant incoming perceptual stimuli. To this aim, we manipulated the contrast of a stimulus presented during the maintenance period of a WM task. The to-be-detected stimulus could indicate which of the encoded representations was going to be probed after the delay (spatial retro-cue) or could signal that all the representations had equal probability to be tested (neutral retro-cue). Time-frequency analysis revealed that alpha power preceding retro-cue presentation was not differently modulated by the two different contrast conditions. This is, participants did not endogenously modulate alpha oscillations upon low perceptual contrast stimuli incoming. These results suggest that alpha delay activity is not a goal directed mechanism to control the inflow of information during WM maintenance. Instead, current data suggest that alpha delayed activity might be an index of increased allocation of attentional resources to the processing of the WM representations.

Thalamic deep brain stimulation paradigm to reduce consciousness: Cortico-striatal dynamics implicated in mechanisms of consciousness

Anesthetic manipulations provide much-needed causal evidence for neural correlates of consciousness, but non-specific drug effects complicate their interpretation. Evidence suggests that thalamic deep brain stimulation (DBS) can either increase or decrease consciousness, depending on the stimulation target and parameters. The putative role of the central lateral thalamus (CL) in consciousness makes it an ideal DBS target to manipulate circuit-level mechanisms in cortico-striato-thalamic (CST) systems, thereby influencing consciousness and related processes. We used multi-microelectrode DBS targeted to CL in macaques while recording from frontal, parietal, and striatal regions. DBS induced episodes of abnormally long, vacant staring with low-frequency oscillations here termed vacant, perturbed consciousness (VPC). DBS modulated VPC likelihood in a frequency-specific manner. VPC events corresponded to decreases in measures of neural complexity (entropy) and integration (Φ*), proposed indices of consciousness, and substantial changes to communication in CST circuits. During VPC, power spectral density and coherence at low frequencies increased across CST circuits, especially in thalamo-parietal and cortico-striatal pathways. Decreased consciousness and neural integration corresponded to shifts in cortico-striatal network configurations that dissociated parietal and subcortical structures. Overall, the features of VPC and implicated networks were similar to those of absence epilepsy. As this same multi-microelectrode DBS method-but at different stimulation frequencies-can also increase consciousness in anesthetized macaques, it can be used to flexibly address questions of consciousness with limited confounds, as well as inform clinical investigations of other consciousness disorders.

Spontaneous Alpha-Band Oscillations Bias Subjective Contrast Perception

Perceptual decisions depend both on the features of the incoming stimulus and on the ongoing brain activity at the moment the stimulus is received. Specifically, trial-to-trial fluctuations in cortical excitability have been linked to fluctuations in the amplitude of prestimulus α oscillations (∼8-13 Hz), which are in turn are associated with fluctuations in subjects' tendency to report the detection of a stimulus. It is currently unknown whether α oscillations bias postperceptual decision-making, or even bias subjective perception itself. To answer this question, we used a contrast discrimination task in which both male and female human subjects reported which of two gratings (one in each hemifield) was perceived as having a stronger contrast. Our EEG analysis showed that subjective contrast was reduced for the stimulus in the hemifield represented in the hemisphere with relatively stronger prestimulus α amplitude, reflecting reduced cortical excitability. Furthermore, the strength of this spontaneous hemispheric lateralization was strongly correlated with the magnitude of individual subjects' biases, suggesting that the spontaneous patterns of α lateralization play a role in explaining the intersubject variability in contrast perception. These results indicate that spontaneous fluctuations in cortical excitability, indicated by patterns of prestimulus α amplitude, affect perceptual decisions by altering the phenomenological perception of the visual world.SIGNIFICANCE STATEMENT Our moment-to-moment perception of the world is shaped by the features of the environment surrounding us, as much as by the constantly evolving states that characterize our brain activity. Previous research showed how the ongoing electrical activity of the brain can influence whether a stimulus has accessed conscious perception. However, evidence is currently missing on whether these electrical brain states can be associated to the subjective experience of a sensory input. Here we show that local changes in patterns of electrical brain activity preceding visual stimulation can bias our phenomenological perception. Importantly, we show that the strength of these variations can help explain the great interindividual variability in how we perceive the visual environment surrounding us.

Biophysical mechanism underlying compensatory preservation of neural synchrony over the adult lifespan

We propose that the preservation of functional integration, estimated from measures of neural synchrony, is a key objective of neurocompensatory mechanisms associated with healthy human ageing. To support this proposal, we demonstrate how phase-locking at the peak alpha frequency in Magnetoencephalography recordings remains invariant over the lifespan in a large cohort of human participants, aged 18-88 years. Using empirically derived connection topologies from diffusion tensor imaging data, we create an in-silico model of whole-brain alpha dynamics. We show that enhancing inter-areal coupling can cancel the effect of increased axonal transmission delays associated with age-related degeneration of white matter tracts, albeit at slower network frequencies. By deriving analytical solutions for simplified connection topologies, we further establish the theoretical principles underlying compensatory network re-organization. Our findings suggest that frequency slowing with age- frequently observed in the alpha band in diverse populations- may be viewed as an epiphenomenon of the underlying compensatory mechanism.

Analysis of MEG data from healthy participants and whole-brain network modeling suggests that the brain compensates for age-related disruptions in connectivity by slowing down the frequency of neural synchronization.

Alterations in resting-state functional connectivity after brain posterior lesions reflect the functionality of the visual system in hemianopic patients

Emerging evidence suggests a role of the posterior cortices in regulating alpha oscillatory activity and organizing low-level processing in non-alpha frequency bands. Therefore, posterior brain lesions, which damage the neural circuits of the visual system, might affect functional connectivity patterns of brain rhythms. To test this hypothesis, eyes-closed resting state EEG signal was acquired from patients with hemianopia with left and right posterior lesions, patients without hemianopia with more anterior lesions and healthy controls. Left-lesioned hemianopics showed reduced intrahemispheric connectivity in the range of upper alpha only in the lesioned hemisphere, whereas right-lesioned hemianopics exhibited reduced intrahemispheric alpha connectivity in both hemispheres. In terms of network topology, these impairments were characterized by reduced local functional segregation, with no associated change in global functional integration. This suggests a crucial role of posterior cortices in promoting functional connectivity in the range of alpha. Right-lesioned hemianopics revealed also additional impairments in the theta range, with increased connectivity in this frequency band, characterized by both increased local segregated activity and decreased global integration. This indicates that lesions to right posterior cortices lead to stronger impairments in alpha connectivity and induce additional alterations in local and global low-level processing, suggesting a specialization of the right hemisphere in generating alpha oscillations and in coordinating complex interplays with lower frequency bands. Importantly, hemianopic patient's visual performance in the blind field was linked to alpha functional connectivity, corroborating the notion that alpha oscillatory patterns represent a biomarker of the integrity and the functioning of the underlying visual system.

Spontaneous alpha-band amplitude predicts subjective visibility but not discrimination accuracy during high-level perception

Near-threshold perception is a paradigm case of awareness diverging from reality - the perception of an unchanging stimulus can vacillate from undetected to clearly perceived. The amplitude of low-frequency brain oscillations - particularly in the alpha-band (8-13 Hz) - has emerged as a reliable predictor of trial-to-trial variability in perceptual decisions based on simple, low-level stimuli. Here, we addressed the question of how spontaneous oscillatory amplitude impacts subjective and objective aspects of perception using high-level visual stimuli. Human observers completed a near-threshold face/house discrimination task with subjective visibility ratings while electroencephalograms (EEG) were recorded. Using single-trial multiple regression analysis, we found that spontaneous fluctuations in prestimulus alpha-band amplitude were negatively related to visibility judgments but did not predict trial-by-trial accuracy. These results extend previous findings that indicate that strong prestimulus alpha diminishes subjective perception without affecting the accuracy or sensitivity (d') of perceptual decisions into the domain of high-level perception.

Oscillatory brain activity links experience to expectancy during associative learning

Associating a novel situation with a specific outcome involves a cascade of cognitive processes, including selecting relevant stimuli, forming predictions regarding expected outcomes, and updating memorized predictions based on experience. The present manuscript uses computational modeling and machine learning to test the hypothesis that alpha-band (8-12 Hz) oscillations are involved in the updating of expectations based on experience. Participants learned that a visual cue predicted an aversive loud noise with a probability of 50%. The Rescorla-Wagner model of associative learning explained trial-wise changes in self-reported noise expectancy as well as alpha power changes. Experience in the past trial and self-reported expectancy for the subsequent trial were accurately decoded based on the topographical distribution of alpha power at specific latencies. Decodable information during initial association formation and contingency report recurred when viewing the conditioned cue. Findings support the idea that alpha oscillations have multiple, temporally specific, roles in the formation of associations between cues and outcomes.

Alpha-band EEG suppression as a neural marker of sustained attentional engagement to conditioned threat stimuli

Attention helps us to be aware of the external world, and this may be especially important when a threat stimulus predicts an aversive outcome. Electroencephalogram (EEG) alpha-band suppression has long been considered as a neural signature of attentional engagement. The present study was designed to test whether attentional engagement, as indexed by alpha-band suppression, is increased in a sustained manner following a conditioned stimulus (CS) that is paired with an aversive (CS+) vs neutral (CS-) outcome. We tested 70 healthy young adults in aversive conditioning and extinction paradigms. One of three colored circles served as the CS+, which was paired in 50% of the trials with a noise burst (unconditioned stimulus, US). The other colored circles (CS-) were never paired with the US. For conditioning, we found greater alpha-band suppression for the CS+ compared to the CS-; this suppression was sustained through the time of the predicted US. This effect was significantly reduced for extinction. These results indicate that conditioned threat stimuli trigger an increase in attentional engagement as subjects monitor the environment for the predicted aversive stimulus. Moreover, this alpha-band suppression effect may be valuable for future studies examining normal or pathological increases in attentional monitoring following threat stimuli.

Alpha blocking and 1/fβ spectral scaling in resting EEG can be accounted for by a sum of damped alpha band oscillatory processes

The dynamical and physiological basis of alpha band activity and 1/f^β noise in the EEG are the subject of continued speculation. Here we conjecture, on the basis of empirical data analysis, that both of these features may be economically accounted for through a single process if the resting EEG is conceived of being the sum of multiple stochastically perturbed alpha band damped linear oscillators with a distribution of dampings (relaxation rates). The modulation of alpha-band and 1/f^β noise activity by changes in damping is explored in eyes closed (EC) and eyes open (EO) resting state EEG. We aim to estimate the distribution of dampings by solving an inverse problem applied to EEG power spectra. The characteristics of the damping distribution are examined across subjects, sensors and recording condition (EC/EO). We find that there are robust changes in the damping distribution between EC and EO recording conditions across participants. The estimated damping distributions are found to be predominantly bimodal, with the number and position of the modes related to the sharpness of the alpha resonance and the scaling (β) of the power spectrum (1/f^β). The results suggest that there exists an intimate relationship between resting state alpha activity and 1/f^β noise with changes in both governed by changes to the damping of the underlying alpha oscillatory processes. In particular, alpha-blocking is observed to be the result of the most weakly damped distribution mode becoming more heavily damped. The results suggest a novel way of characterizing resting EEG power spectra and provides new insight into the central role that damped alpha-band activity may play in characterising the spatio-temporal features of resting state EEG.

The resting human electroencephalogram (EEG) exhibits two dominant spectral features: the alpha rhythm (8–13 Hz) and its associated attenuation between eyes-closed and eyes-open resting state (alpha blocking), and the 1/f^β scaling of the power spectrum. While these phenomena are well studied a thorough understanding of their respective generative processes remains elusive. By employing a theoretical approach that follows from neural population models of EEG we demonstrate that it is possible to economically account for both of these phenomena using a singular mechanistic framework: resting EEG is assumed to arise from the summed activity of multiple uncorrelated, stochastically driven, damped alpha band linear oscillatory processes having a distribution of relaxation rates or dampings. By numerically estimating these damping distributions from eyes-closed and eyes-open EEG data, in a total of 136 participants, it is found that such damping distributions are predominantly bimodal in shape. The most weakly damped mode is found to account for alpha band power, with alpha blocking being driven by an increase in the damping of this weakly damped mode, whereas the second, and more heavily damped mode, is able to explain 1/f^β scaling present in the resting state EEG spectra.

"We will be in touch". A neuroscientific assessment of remote vs. face-to-face job interviews via EEG hyperscanning

In the last decades, improving remote communications in companies has been a compelling issue. With the outspread of SARS-CoV-2 pandemic, this phenomenon has undergone an acceleration. Despite this, little to no research, considering neurocognitive and emotional systems, was conducted on job interview, a critical organizational phase which significantly contributes to a company long-term success.In this study, we aimed at exploring the emotional and cognitive processes related to different phases of a job interview (introductory, attitudinal, technical and conclusion), when considering two conditions: face-to-face and remote, by simultaneously gathering EEG (frequency bands: alpha, beta, delta, and theta) and autonomic data (skin-conductance-level, SCL, skin-conductance-response, SCR, and heart rate, HR) in both candidates and recruiters. Data highlighted a generalized alpha desynchronization during the job interview interaction. Recruiters showed increased frontal theta activity, which is connected to socio-emotional situations and emotional processing. In addition, results showed how face-to-face condition is related to increased SCL and theta power in the central-brain area, associated with learning processes, via the mid-brain dopamine system and the anterior cingulate cortex. Furthermore, we found higher HR in the candidates. Present results call to re-examine the impact of information-technology in the organization, opening to translational opportunities.

1024-channel electrophysiological recordings in macaque V1 and V4 during resting state

Co-variations in resting state activity are thought to arise from a variety of correlated inputs to neurons, such as bottom-up activity from lower areas, feedback from higher areas, recurrent processing in local circuits, and fluctuations in neuromodulatory systems. Most studies have examined resting state activity throughout the brain using MRI scans, or observed local co-variations in activity by recording from a small number of electrodes. We carried out electrophysiological recordings from over a thousand chronically implanted electrodes in the visual cortex of non-human primates, yielding a resting state dataset with unprecedentedly high channel counts and spatiotemporal resolution. Such signals could be used to observe brain waves across larger regions of cortex, offering a temporally detailed picture of brain activity. In this paper, we provide the dataset, describe the raw and processed data formats and data acquisition methods, and indicate how the data can be used to yield new insights into the ‘background’ activity that influences the processing of visual information in our brain.

Measurement(s)	brain activity measurement • eye movement
Technology Type(s)	extracellular electrophysiology recording • multi-electrode array system • infrared camera
Factor Type(s)	visual stimulation
Sample Characteristic - Organism	Macaca mulatta