The generation and propagation of the human alpha rhythm

Alpha-band activity in density spectral array predictive for neurological outcome in patients with hypoxic-ischemic encephalopathy

BACKGROUND

In patients with hypoxic-ischemic encephalopathy (HIE), EEG is used to predict outcomes. However, a clear threshold for EEG findings associated with favorable outcomes remains unestablished. This study evaluates the predictive value of density spectral array (DSA)-based background activity in HIE patients.

METHODS

Forty-four consecutive HIE patients with disturbance of consciousness (2010-2023) were retrospectively assessed and categorized into highly malignant, malignant, or benign EEG patterns according to the conventional EEG classification. The presence of alpha-band activity, defined as an increase in alpha (or theta) frequency band power visible in the DSA, was also assessed. The relationship among conventional EEG classification, alpha-band activity, and neurological outcomes was evaluated.

RESULTS

All patients with highly malignant EEG lacked alpha-band activity and experienced poor outcomes, whereas those with less severe patterns occasionally exhibited alpha-band activity (14 % in the malignant vs. 60 % in the benign, p = 0.021), and demonstrated various outcomes. Recovery of consciousness until discharge was more prominent in patients with alpha-band activity compared to those without (100 % vs. 39 %, p < 0.001).

CONCLUSIONS

DSA-based evaluations provide a simple and valuable tool for predicting favorable neurological outcomes.

Alpha rhythm and Alzheimer's disease: Has Hans Berger's dream come true?

In this "centenary" paper, an expert panel revisited Hans Berger's groundbreaking discovery of human restingstate electroencephalographic (rsEEG) alpha rhythms (8-12 Hz) in 1924, his foresight of substantial clinical applications in patients with "senile dementia," and new developments in the field, focusing on Alzheimer's disease (AD), the most prevalent cause of dementia in pathological aging. Clinical guidelines issued in 2024 by the US National Institute on Aging-Alzheimer's Association (NIA-AA) and the European Neuroscience Societies did not endorse routine use of rsEEG biomarkers in the clinical workup of older adults with cognitive impairment. Nevertheless, the expert panel highlighted decades of research from independent workgroups and different techniques showing consistent evidence that abnormalities in rsEEG delta, theta, and alpha rhythms (< 30 Hz) observed in AD patients correlate with wellestablished AD biomarkers of neuropathology, neurodegeneration, and cognitive decline. We posit that these abnormalities may reflect alterations in oscillatory synchronization within subcortical and cortical circuits, inducing cortical inhibitory-excitatory imbalance (in some cases leading to epileptiform activity) and vigilance dysfunctions (e.g., mental fatigue and drowsiness), which may impact AD patients' quality of life. Berger's vision of using EEG to understand and manage dementia in pathological aging is still actual.

Fmr1 KO causes delayed rebound spike timing in mediodorsal thalamocortical neurons through regulation of HCN channel activity

Background

The neurodevelopmental disorder Fragile X syndrome (FXS) results from hypermethylation of the FMR1 gene which prevents FMRP production. FMRP modulates the expression and function of a wide variety of proteins, including voltage-gated ion channels such as Hyperpolarization-Activated Cyclic Nucleotide gated (HCN) channels, which are integral to rhythmic activity in thalamic structures. Thalamocortical pathology, particularly involving the mediodorsal thalamus (MD), has been implicated in neurodevelopmental disorders. MD connectivity with mPFC is integral to executive functions like working memory and social behaviors that are disrupted in FXS.

Methods

We used a combination of retrograde labeling and ex vivo brain slice whole cell electrophysiology in 40 wild type and 42 Fmr1 KO male mice to investigate how a lack of Fmr1 affects intrinsic cellular properties in lateral (MD-L) and medial (MD-M) MD neurons that project to the medial prefrontal cortex (MD→mPFC neurons).

Results

In MD-L neurons, Fmr1 knockout caused a decrease in HCN-mediated membrane properties such as voltage sag and membrane afterhyperpolarization. These changes in subthreshold properties were accompanied by changes in suprathreshold neuron properties such as the variability of action potential burst timing.

Conclusions

In Fmr1 knockout mice, reduced HCN channel activity in MD→mPFC neurons impairs both the timing and magnitude of HCN-mediated membrane potential regulation. Changes in response timing may adversely affect rhythm propagation in Fmr1 KO thalamocortical circuitry. MD thalamic neurons are critical for maintaining rhythmic activity involved in cognitive and affective functions. Understanding specific mechanisms of thalamocortical circuit activity may lead to therapeutic interventions for individuals with FXS.

Metacognition in the listening brain

How do you know you have heard right? Metacognition, the ability to assess and monitor one's own cognitive state, is key to understanding human communication in complex environments. However, the foundational role of metacognition in hearing and communication is only beginning to be explored, and the neuroscience behind it is an emerging field: how does confidence express in neural dynamics of the listening brain? What is known about auditory metaperceptual alterations as a hallmark phenomenon in psychosis, dementia, or hearing loss? Building on Bayesian ideas of auditory perception and auditory neuroscience, 'meta-listening' offers a framework for more comprehensive research into how metacognition in humans and non-humans shapes the listening brain.

Hyperarousal features in the sleep architecture of individuals with and without insomnia

Sleep architecture encodes relevant information on the structure of sleep and has been used to assess hyperarousal in insomnia. This study investigated whether polysomnography-derived sleep architecture displays signs of hyperarousal in individuals with insomnia compared with individuals without insomnia. Data from Phase 3 clinical trials, private clinics and a cohort study were analysed. A comprehensive set of sleep architecture features previously associated with hyperarousal were retrospectively analysed focusing on sleep-wake transition probabilities, electroencephalographic spectra and sleep spindles, and enriched with a novel machine learning algorithm called the Wake Electroencephalographic Similarity Index. This analysis included 1710 individuals with insomnia and 1455 individuals without insomnia. Results indicate that individuals with insomnia had a higher likelihood of waking from all sleep stages, and showed increased relative alpha during Wake and N1 sleep and increased theta power during Wake when compared with individuals without insomnia. Relative delta power was decreased and Wake Electroencephalographic Similarity Index scores were elevated across all sleep stages except N3, suggesting more wake-like activity during these stages in individuals with insomnia. Additionally, sleep spindle density was decreased, and spindle dispersion was increased in individuals with insomnia. These findings suggest that insomnia is characterized by a dysfunction in sleep quality with a continuous hyperarousal, evidenced by changes in sleep-wake architecture.

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.

Touch-evoked traveling waves establish a translaminar spacetime code

Linking sensory-evoked traveling waves to underlying circuit patterns is critical to understanding the neural basis of sensory perception. To form this link, we performed simultaneous electrophysiology and two-photon calcium imaging through transparent NeuroGrids and mapped touch-evoked traveling waves and underlying microcircuit dynamics. In awake mice, both passive and active whisker touch elicited traveling waves within and across barrels, with a fast early component followed by a late wave that lasted hundreds of milliseconds poststimulus. Notably, late waves were modulated by perceived value and predicted behavioral choice in a two-whisker discrimination task. We found that the late wave feature was (i) modulated by motor feedback, (ii) differentially engaged a sparse ensemble reactivation pattern across layer 2/3, which a balanced-state network model reconciled via feedback-induced inhibitory stabilization, and (iii) aligned to regenerative layer 5 apical dendritic Ca2+ events. Our results reveal that translaminar spacetime patterns organized by cortical feedback support sparse touch-evoked traveling waves.

The complexity of caffeine's effects on regular coffee consumers

Why does coffee wake us up? Is it because it contains caffeine, or because we are used to it waking us up after drinking it? To answer this question, we recruited twenty habitual coffee drinkers who received either caffeinated or decaffeinated coffee (placebo) in a double-blind, randomized fashion. The two substances were identical except for the presence of caffeine. We measured cognitive performance, cardiovascular responses, and whole-head EEG during rest and during an auditory-oddball task. The same measurements were done before and after ingestion. We expected to find significant differences between caffeine and placebo groups across the outcome measures. However, except for the resting-state alpha power, changes due to ingestion in physiological responses and in cognitive functioning were not significantly different between the two groups. Actually, only one of the three cognitive measures was found to be significantly altered by the ingestion. These findings suggest that regular coffee consumers respond to coffee-like beverages independently of the presence of caffeine.

Photic drive response in people with epilepsy: Exploring the interaction with background alpha rhythm

Photic drive responses (PDRs) are used to explore cortical hyperexcitability. We quantified PDRs and interactions with the alpha rhythm in people with epilepsy (PwE). Fifteen PwE (mean age ± SD 47.3 ± 4.6 years; 8 males), and 15 control subjects (mean age 52.7 ± 4.6 years; 9 males) underwent EEG with modified intermittent photic stimulation (IPS). The modification allowed so-called alpha-band gain to be measured. None of the PwE had demonstrated photosensitivity. The modified IPS method alternated eyes-open and eyes-closed conditions with and without IPS. The alpha-band gain appeared as N-fold changes in PDR when IPS (or its harmonics) and the alpha-bands overlapped. An epileptic attack within 1 month of testing significantly increased alpha-band gain by 1.36×. Generalised epilepsy (but not focal epilepsy) significantly decreased alpha-band gain y 0.79×. Each decade of age beyond the mean age significantly increased alpha-band gain by 1.09×. Similar significant interactions were seen between alpha and the second harmonic of IPS driving frequencies that matched alpha frequencies, i.e. for recent attack and, generalized epilepsy. The interactions thus appeared to be occurring between cortical IPS outputs and the alpha generator. These changes were most evident at electrodes O1 and O2. Investigating alpha-band gain using modified IPS offers a way to quantify cortical hyperexcitability in epilepsy and other diseases. It also provides new information about alpha and so too predictive coding, which appears to be at least partly governed by alpha.

Pigs as a translational animal model for the study of peak alpha frequency

The most characteristic feature of the human electroencephalogram is the peak alpha frequency (PAF). While PAF has been proposed as a biomarker in several diseases and disorders, the disease mechanisms modulating PAF, as well as its physiological substrates, remain elusive. This has partly been due to challenges related to experimental manipulation and invasive procedures in human neuroscience, as well as the scarcity of animal models where PAF is consistently present in resting-state. With the potential inclusion of PAF in clinical screening and decision-making, advancing the mechanistic understanding of PAF is warranted. In this paper, we propose the female Danish Landrace pig as a suitable animal model to probe the mechanisms of PAF and its feature as a biomarker. We show that somatosensory alpha oscillations are present in anesthetized pigs using electrocorticography and intracortical electrodes located at the sensorimotor cortex. This was evident when looking at the time-domain as well as the spectral morphology of spontaneous recordings. We applied the FOOOF-algorithm to extract the spectral characteristics and implemented a robustness threshold for any periodic component. Using this conservative threshold, PAF was present in 18/20 pigs with a normal distribution of the peak frequency between 8-12 Hz, producing similar findings to human recordings. We show that PAF was present in 69.6 % of epochs of approximately six-minute-long resting-state recordings. In sum, we propose that the pig is a suitable candidate for investigating the neural mechanisms of PAF as a biomarker for disease and disorders such as pain, neuropsychiatric disorders, and response to pharmacotherapy.

Precision data-driven modeling of cortical dynamics reveals person-specific mechanisms underpinning brain electrophysiology

Task-free brain activity affords unique insight into the functional structure of brain network dynamics and has been used to identify neural markers of individual differences. In this work, we present an algorithmic optimization framework that directly inverts and parameterizes brain-wide dynamical-systems models involving hundreds of interacting neural populations, from single-subject M/EEG time-series recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics ("precision brain models") and making quantitative predictions. We extensively validate the models' performance in forecasting future brain activity and predicting individual variability in key M/EEG metrics. Last, we demonstrate the power of our technique in resolving individual differences in the generation of alpha and beta-frequency oscillations. We characterize subjects based upon model attractor topology and a dynamical-systems mechanism by which these topologies generate individual variation in the expression of alpha vs. beta rhythms. We trace these phenomena back to global variation in excitatory-inhibitory balance, highlighting the explanatory power of our framework to generate mechanistic insights.

Electrophysiological Signatures of Alpha Coma

PURPOSE

Recent research on quantitative EEG in coma has proposed several metrics correlating with consciousness level. However, the heterogeneous nature of coma can challenge the generalizability of these measures. This study investigates alpha-coma, an electroclinical pattern characterized by a widespread, nonreactive alpha rhythm often linked to poor outcomes. The aim was to quantify the electrophysiological features of alpha-coma and compare them to the alpha rhythm in awake controls, seeking clearer insights into quantitative EEG analysis in comatose states.

METHODS

Fourteen alpha-coma patients were retrospectively selected from University Hospitals of Geneva and age-matched with 14 healthy control subjects from an open-source dataset. EEG data were preprocessed and analyzed to extract power spectra, spectral decay (aperiodic activity), sample entropy, and functional connectivity.

RESULTS

Alpha-coma patients did not differ in alpha power but exhibited significantly higher levels of spectral decay ( p < 0.001), suggesting a convergence toward an inhibitory state. Sample entropy was significantly higher in alpha-coma patients ( p = 0.01), indicating an increase in the cortical complexity in alpha-coma compared with healthy subjects.

CONCLUSIONS

Alpha-coma shows increased aperiodic activity and EEG complexity, despite similar alpha power and clustering coefficient. The increased aperiodic activity aligns with findings in other comatose patients, including those sedated or with subcortical dysfunction. However, the increased entropy contradicts existing literature, suggesting that alpha-coma may represent a state of widespread cortical dysfunction likely resulting from nonhierarchical, turbulent brain activity. This indicates that the loss of consciousness does not guarantee consistent cortical measures across the whole spectrum of EEG patterns.

Detection of respiratory frequency rhythm in human alpha phase shifts: topographic distributions in wake and drowsy states

Introduction

The relationship between brain activity and respiration is recently attracting increasing attention, despite being studied for a long time. Respiratory modulation was evidenced in both single-cell activity and field potentials. Among EEG and intracranial measurements, the effect of respiration was prevailingly studied on amplitude/power in all frequency bands.

Methods

Since phases of EEG oscillations received less attention, we applied our previously published carrier frequency (CF) mathematical model of human alpha oscillations on a group of 10 young healthy participants in wake and drowsy states, using a 14-channel average reference montage. Since our approach allows for a more precise calculation of CF phase shifts (CFPS) than any individual Fourier component, by using a 2-s moving Fourier window, we validated the new method and studied, for the first time, temporal waveforms CFPS(t) and their oscillatory content through FFT (CFPS(t)).

Results

Although not appearing equally in all channel pairs and every subject, a clear peak in the respiratory frequency region, 0.21-0.26 Hz, was observed (max at 0.22 Hz). When five channel pairs with the most prominent group averaged amplitudes at 0.22 Hz were plotted in both states, topographic distributions changed significantly-from longitudinal, connecting frontal and posterior channels in the wake state to topographically split two separate regions-frontal and posterior in the drowsy state. In addition, in the drowsy state, 0.22-Hz amplitudes decreased for all pairs, while statistically significant reduction was obtained for 20/91 (22%) pairs.

Discussion

These results potentially evidence, for the first time, the respiratory frequency modulation of alpha phase shifts, as well as the significant impact of wakeful consciousness on the observed oscillations.

The effect of mobile phone electromagnetic fields on the human resting state wake EEG and event-related potential: A systematic review and meta-analysis

The rapid growth of mobile phone usage and its use of radiofrequency electromagnetic fields (RF-EMF) have raised concerns about potential health risks. Researchers have conducted studies to examine the effects of RF-EMF on the brain using electroencephalography (EEG). We conducted a systematic quality assessment and meta-analysis of published research in this field to establish high-quality studies as references for future protocols. The electronic search yielded 244 records from which a total of 51 studies were included in the review after excluding studies based on study design, and data or report availability. Of these 51 studies, 31 (61%) focused on resting state wake EEG and 20 (39%) on event-related potentials (ERP). None of the 51 studies were free from risk of bias. From the 51 included studies, we were able to use seven studies to create three different groups for meta-analysis for resting state wake EEG and five studies to create 10 different groups for meta-analysis for ERP. Per group the number of studies varies from 1 to 5. Our procedure is the first systematic quality assessment in this field and revealed three important findings. First, there is evidence of an effect on the EEG of a 2G protocol using an eyes-open condition. Second, we did not find evidence for EEG effects during task performance. This suggests that the impact of EMF during task performance is less pronounced compared to the resting state condition. Third, this meta-analysis shows that the field is unable to create an evidence base for most comparisons due to heterogeneity. We therefore advise that all future studies are double-blind in nature, adhere to the methodological standard of randomized experiments, and publish their protocols first.

Multisensory mechanisms of gait and balance in Parkinson's disease: an integrative review

Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have on our aging population. Posture and gait control does not happen automatically, as previously believed, but rather requires continuous involvement of central nervous mechanisms. To effectively exert control over the body, the brain must integrate multiple streams of sensory information, including visual, vestibular, and somatosensory signals. The mechanisms which underpin the integration of these multisensory signals are the principal topic of the present work. Existing multisensory integration theories focus on how failure of cognitive processes thought to be involved in multisensory integration leads to falls in older adults. Insufficient emphasis, however, has been placed on specific contributions of individual sensory modalities to multisensory integration processes and cross-modal interactions that occur between the sensory modalities in relation to gait and balance. In the present work, we review the contributions of somatosensory, visual, and vestibular modalities, along with their multisensory intersections to gait and balance in older adults and patients with Parkinson's disease. We also review evidence of vestibular contributions to multisensory temporal binding windows, previously shown to be highly pertinent to fall risk in older adults. Lastly, we relate multisensory vestibular mechanisms to potential neural substrates, both at the level of neurobiology (concerning positron emission tomography imaging) and at the level of electrophysiology (concerning electroencephalography). We hope that this integrative review, drawing influence across multiple subdisciplines of neuroscience, paves the way for novel research directions and therapeutic neuromodulatory approaches, to improve the lives of older adults and patients with neurodegenerative diseases.

Short report on a distinct electroencephalogram endophenotype for MTHFR gene variation co-occurring in autism spectrum disorder

LAY ABSTRACT

Methylenetetrahydrofolate reductase mutations refer to genetic variations in the methylenetetrahydrofolate reductase enzyme, which plays an important role in folate metabolism. Folate is essential for neural development and signalling. Children with autism spectrum disorder have atypical neural signals compared with control. This study used a non-invasive method to identify a distinct neural signal that may be useful in future screening for methylenetetrahydrofolate reductase mutation in children with autism spectrum disorder. Given that the underlying causes of autism spectrum disorder have multiple genetic factors and often require subjective assessment, this study introduces a potential non-invasive screening method for methylenetetrahydrofolate reductase gene mutation. This method could provide valuable biomarkers for screening and personalised treatments, offering hope for improved risk stratification and bespoke nutritional support and supplements to mitigate the impact on affected individuals and their descendants.

Alpha Traveling Waves during Working Memory: Disentangling Bottom-Up Gating and Top-Down Gain Control

While previous works established the inhibitory role of alpha oscillations during working memory maintenance, it remains an open question whether such an inhibitory control is a top-down process. Here, we attempted to disentangle this issue by considering the spatiotemporal component of waves in the alpha band, i.e., alpha traveling waves. We reanalyzed two pre-existing and open-access EEG datasets (N = 180, 90 males, 80 females, 10 unknown) where participants performed lateralized, visual delayed match-to-sample working memory tasks. In the first dataset, the distractor load was manipulated (2, 4, or 6), whereas in the second dataset, the memory span varied between 1, 3, and 6 items. We focused on the propagation of alpha waves on the anterior-posterior axis during the retention period. Our results reveal an increase in alpha-band forward waves as the distractor load increased, but also an increase in forward waves and a decrease in backward waves as the memory set size increased. Our results also showed a lateralization effect: alpha forward waves exhibited a more pronounced increase in the hemisphere contralateral to the distractors, whereas the reduction in backward waves was stronger in the hemisphere contralateral to the targets. In short, the forward waves were regulated by distractors, whereas targets inversely modulated backward waves. Such a dissociation of goal-related and goal-irrelevant physiological signals suggests the coexistence of bottom-up and top-down inhibitory processes: alpha forward waves might convey a gating effect driven by distractor load, while backward waves may represent direct top-down gain control of downstream visual areas.

Brain fingerprint and subjective mood state across the menstrual cycle

Background

Brain connectome fingerprinting represents a recent and valid approach in assessing individual identifiability on the basis of the subject-specific brain functional connectome. Although this methodology has been tested and validated in several neurological diseases, its performance, reliability and reproducibility in healthy individuals has been poorly investigated. In particular, the impact of the changes in brain connectivity, induced by the different phases of the menstrual cycle (MC), on the reliability of this approach remains unexplored. Furthermore, although the modifications of the psychological condition of women during the MC are widely documented, the possible link with the changes of brain connectivity has been poorly investigated.

Methods

We conducted the Clinical Connectome Fingerprint (CCF) analysis on source-reconstructed magnetoencephalography signals in a cohort of 24 women across the MC.

Results

All the parameters of identifiability did not differ according to the MC phases. The peri-ovulatory and mid-luteal phases showed a less stable, more variable over time, brain connectome compared to the early follicular phase. This difference in brain connectome stability in the alpha band significantly predicted the self-esteem level (p-value <0.01), mood (p-value <0.01) and five (environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance) of the six dimensions of well-being (p-value <0.01, save autonomy).

Conclusion

These results confirm the high reliability of the CCF as well as its independence from the MC phases. At the same time the study provides insights on changes of the brain connectome in the different phases of the MC and their possible role in affecting women's subjective mood state across the MC. Finally, these changes in the alpha band share a predictive power on self-esteem, mood and well-being.

Alpha rhythm slowing in temporal lobe epilepsy across scalp EEG and MEG

EEG slowing is reported in various neurological disorders including Alzheimer's, Parkinson's and Epilepsy. Here, we investigate alpha rhythm slowing in individuals with refractory temporal lobe epilepsy compared with healthy controls, using scalp EEG and magnetoencephalography. We retrospectively analysed data from 17 (46) healthy controls and 22 (24) individuals with temporal lobe epilepsy who underwent scalp EEG and magnetoencephalography recordings as part of presurgical evaluation. Resting-state, eyes-closed recordings were source reconstructed using the standardized low-resolution brain electrographic tomography method. We extracted slow 6-9 Hz and fast 10-11 Hz alpha relative band power and calculated the alpha power ratio by dividing slow alpha by fast alpha. This ratio was computed for all brain regions in all individuals. Alpha oscillations were slower in individuals with temporal lobe epilepsy than controls (P< 0.05). This effect was present in both the ipsilateral and contralateral hemispheres and across widespread brain regions. Alpha slowing in temporal lobe epilepsy was found in both EEG and magnetoencephalography recordings. We interpret greater slow alpha as greater deviation from health.

Proximity to Explosive Synchronization Determines Network Collapse and Recovery Trajectories in Neural and Economic Crises

When complex systems move away from criticality-a balance between order and chaos-they are no longer optimized. Furthermore, when criticality is lost too quickly, or recovery is delayed, system damage can result. However, the mechanism for these abnormally fast or slow critical transitions remains unknown. Here, we show that the proximity of a complex network to explosive synchronization (ES), a first-order phase transition, determines the trajectories of criticality loss and recovery after perturbations. Our computational models revealed characteristic dynamics based on network proximity to ES, enabling us to infer network phase transition types from empirical data and predict criticality transition patterns. We validated our predictions using empirical data from the human brain under anesthesia and the stock market during an economic crisis, demonstrating that early and prolonged recoveries can be systematically predicted. This study has implications for designing resilient networks that withstand perturbations and recover quickly.

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.

Beyond neurons and spikes: cognon, the hierarchical dynamical unit of thought

From the dynamical point of view, most cognitive phenomena are hierarchical, transient and sequential. Such cognitive spatio-temporal processes can be represented by a set of sequential metastable dynamical states together with their associated transitions: The state is quasi-stationary close to one metastable state before a rapid transition to another state. Hence, we postulate that metastable states are the central players in cognitive information processing. Based on the analogy of quasiparticles as elementary units in physics, we introduce here the quantum of cognitive information dynamics, which we term "cognon". A cognon, or dynamical unit of thought, is represented by a robust finite chain of metastable neural states. Cognons can be organized at multiple hierarchical levels and coordinate complex cognitive information representations. Since a cognon is an abstract conceptualization, we link this abstraction to brain sequential dynamics that can be measured using common modalities and argue that cognons and brain rhythms form binding spatiotemporal complexes to keep simultaneous dynamical information which relate the 'what', 'where' and 'when'.

The Posterior Dominant Rhythm Remains Within Normal Limits in the Microgravity Environment

BACKGROUND

Electroencephalogram (EEG) biomarkers with adequate sensitivity and specificity to reflect the brain's health status can become indispensable for health monitoring during prolonged missions in space. The objective of our study was to assess whether the basic features of the posterior dominant rhythm (PDR) change under microgravity conditions compared to earth-based scalp EEG recordings.

METHODS

Three crew members during the 16-day AXIOM-1 mission to the International Space Station (ISS), underwent scalp EEG recordings before, during, and after the mission by means of a dry-electrode self-donning headgear designed to support long-term EEG recordings in space. Resting-state recordings were performed with eyes open and closed during relaxed wakefulness. The electrodes representative of EEG activity in each occipital lobe were used, and consecutive PDR oscillations were identified during periods of eye closure. In turn, cursor-based markers were placed at the negative peak of each sinusoidal wave of the PDR. Waveform averaging and time-frequency analysis were performed for all PDR samples for the respective pre-mission, mission, and post-mission EEGs.

RESULTS

No significant differences were found in the mean frequency of the PDR in any of the crew subjects between their EEG on the ISS and their pre- or post-mission EEG on ground level. The PDR oscillations varied over a ±1Hz standard deviation range. Similarly, no significant differences were found in PDR's power spectral density.

CONCLUSIONS

Our study shows that the spectral features of the PDR remain within normal limits in a short exposure to the microgravity environment, with its frequency manifesting within an acceptable ±1 Hz variation from the pre-mission mean. Further investigations for EEG features and markers reflecting the human brain neurophysiology during space missions are required.

Electroencephalographic Microstates During Sleep and Wake in Schizophrenia

Background

Aberrant functional connectivity is a hallmark of schizophrenia. The precise nature and mechanism of dysconnectivity in schizophrenia remains unclear, but evidence suggests that dysconnectivity is different in wake versus sleep. Microstate analysis uses electroencephalography (EEG) to investigate large-scale patterns of coordinated brain activity by clustering EEG data into a small set of recurring spatial patterns, or microstates. We hypothesized that this technique would allow us to probe connectivity between brain networks at a fine temporal resolution and uncover previously unknown sleep-specific dysconnectivity.

Methods

We studied microstates during sleep in patients with schizophrenia by analyzing high-density EEG sleep data from 114 patients with schizophrenia and 79 control participants. We used a polarity-insensitive k-means analysis to extract a set of 6 microstate topographies.

Results

These 6 states included 4 widely reported canonical microstates. In patients and control participants, falling asleep was characterized by a shift from microstates A, B, and C to microstates D, E, and F. Microstate F was decreased in patients during wake, and microstate E was decreased in patients during sleep. The complexity of microstate transitions was greater in patients than control participants during wake, but this reversed during sleep.

Conclusions

Our findings reveal behavioral state-dependent patterns of cortical dysconnectivity in schizophrenia. Furthermore, these findings are largely unrelated to previous sleep-related EEG markers of schizophrenia such as decreased sleep spindles. Therefore, these findings are driven by previously undescribed sleep-related pathology in schizophrenia.

Multimodal neuroimaging of hierarchical cognitive control

Cognitive control enables us to translate our knowledge into actions, allowing us to flexibly adjust our behavior, according to environmental contexts, our internal goals, and future plans. Multimodal neuroimaging and neurostimulation techniques have proven essential for advancing our understanding of how cognitive control emerges from the coordination of distributed neuronal activities in the brain. In this review, we examine the literature on multimodal studies of cognitive control. We explore how these studies provide converging evidence for a novel, multiplexed model of cognitive control, in which neural oscillations support different levels of control processing along a functionally hierarchical organization of distinct frontoparietal networks.

Real-time TMS-EEG for brain state-controlled research and precision treatment: a narrative review and guide

Transcranial magnetic stimulation (TMS) modulates neuronal activity, but the efficacy of an open-loop approach is limited due to the brain state's dynamic nature. Real-time integration with electroencephalography (EEG) increases experimental reliability and offers personalized neuromodulation therapy by using immediate brain states as biomarkers. Here, we review brain state-controlled TMS-EEG studies since the first publication several years ago. A summary of experiments on the sensorimotor mu rhythm (8-13 Hz) shows increased cortical excitability due to TMS pulse at the trough and decreased excitability at the peak of the oscillation. Pre-TMS pulse mu power also affects excitability. Further, there is emerging evidence that the oscillation phase in theta and beta frequency bands modulates neural excitability. Here, we provide a guide for real-time TMS-EEG application and discuss experimental and technical considerations. We consider the effects of hardware choice, signal quality, spatial and temporal filtering, and neural characteristics of the targeted brain oscillation. Finally, we speculate on how closed-loop TMS-EEG potentially could improve the treatment of neurological and mental disorders such as depression, Alzheimer's, Parkinson's, schizophrenia, and stroke.

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.

Systematic review of experimental paradigms and deep neural networks for electroencephalography-based cognitive workload detection

This article summarizes a systematic literature review of deep neural network-based cognitive workload (CWL) estimation from electroencephalographic (EEG) signals. The focus of this article can be delineated into two main elements: first is the identification of experimental paradigms prevalently employed for CWL induction, and second, is an inquiry about the data structure and input formulations commonly utilized in deep neural networks (DNN)-based CWL detection. The survey revealed several experimental paradigms that can reliably induce either graded levels of CWL or a desired cognitive state due to sustained induction of CWL. This article has characterized them with respect to the number of distinct CWL levels, cognitive states, experimental environment, and agents in focus. Further, this literature analysis found that DNNs can successfully detect distinct levels of CWL despite the inter-subject and inter-session variability typically observed in EEG signals. Several methodologies were found using EEG signals in its native representation of a two-dimensional matrix as input to the classification algorithm, bypassing traditional feature selection steps. More often than not, researchers used DNNs as black-box type models, and only a few studies employed interpretable or explainable DNNs for CWL detection. However, these algorithms were mostly post hoc data analysis and classification schemes, and only a few studies adopted real-time CWL estimation methodologies. Further, it has been suggested that using interpretable deep learning methodologies may shed light on EEG correlates of CWL, but this remains mostly an unexplored area. This systematic review suggests using networks sensitive to temporal dependencies and appropriate input formulations for each type of DNN architecture to achieve robust classification performance. An additional suggestion is to utilize transfer learning methods to achieve high generalizability across tasks (task-independent classifiers), while simple cross-subject data pooling may achieve the same for subject-independent classifiers.

Alpha coherence is a network signature of cognitive recovery from disorders of consciousness

Alpha (8-12 Hz) frequency band oscillations are among the most informative features in electroencephalographic (EEG) assessment of patients with disorders of consciousness (DoC). Because interareal alpha synchrony is thought to facilitate long-range communication in healthy brains, coherence measures of resting-state alpha oscillations may provide insights into a patient's capacity for higher-order cognition beyond channel-wise estimates of alpha power. In multi-channel EEG, global coherence methods may be used to augment standard spectral analysis methods by both estimating the strength and identifying the structure of coherent oscillatory networks. We performed global coherence analysis in 95 separate clinical EEG recordings (28 healthy controls and 33 patients with acute or chronic DoC, 25 of whom returned for follow-up) collected between two academic medical centers. We found that posterior alpha coherence is associated with recovery of higher-level cognition. We developed a measure of network organization, based on the distance between eigenvectors of the alpha cross-spectral matrix, that detects recovery of posterior alpha networks. In patients who have emerged from a minimally conscious state, we showed that coherence-based alpha networks are reconfigured prior to restoration of alpha power to resemble those seen in healthy controls. This alpha network measure performs well in classifying recovery from DoC (AUC = 0.78) compared to common representations of functional connectivity using the weighted phase lag index (AUC = 0.50 - 0.57). Lastly, we observed that activity within these alpha networks is suppressed during positive responses to task-based EEG command-following paradigms, supporting the potential utility of this biomarker to detect covert cognition. Our findings suggest that restored alpha networks may represent a sensitive early signature of cognitive recovery in patients with DoC. Therefore, network detection methods may augment the utility of EEG assessments for DoC.

Similarity of brain activity patterns during learning and subsequent resting state predicts memory consolidation

Spontaneous reactivation of brain activity from learning to a subsequent off-line period has been implicated as a neural mechanism underlying memory consolidation. However, similarities in brain activity may also emerge as a result of individual, trait-like characteristics. Here, we introduced a novel approach for analyzing continuous electroencephalography (EEG) data to investigate learning-induced changes as well as trait-like characteristics in brain activity underlying memory consolidation. Thirty-one healthy young adults performed a learning task, and their performance was retested after a short (∼1 h) delay. Consolidation of two distinct types of information (serial-order and probability) embedded in the task were tested to reveal similarities in functional networks that uniquely predict the changes in the respective memory performance. EEG was recorded during learning and pre- and post-learning rest periods. To investigate brain activity associated with consolidation, we quantified similarities in EEG functional connectivity between learning and pre-learning rest (baseline similarity) and learning and post-learning rest (post-learning similarity). While comparable patterns of these two could indicate trait-like similarities, changes from baseline to post-learning similarity could indicate learning-induced changes, possibly spontaneous reactivation. Higher learning-induced changes in alpha frequency connectivity (8.5-9.5 Hz) were associated with better consolidation of serial-order information, particularly for long-range connections across central and parietal sites. The consolidation of probability information was associated with learning-induced changes in delta frequency connectivity (2.5-3 Hz) specifically for more local, short-range connections. Furthermore, there was a substantial overlap between the baseline and post-learning similarities and their associations with consolidation performance, suggesting robust (trait-like) differences in functional connectivity networks underlying memory processes.

Electroencephalographic Features of Elderly Patients during Anesthesia Induction with Remimazolam: A Substudy of a Randomized Controlled Trial

BACKGROUND

Although remimazolam is used as a general anesthetic in elderly patients due to its hemodynamic stability, the electroencephalogram characteristics of remimazolam are not well known. The purpose of this study was to identify the electroencephalographic features of remimazolam-induced unconsciousness in elderly patients and compare them with propofol.

METHODS

Remimazolam (n = 26) or propofol (n = 26) were randomly administered for anesthesia induction in surgical patients. The hypnotic agent was blinded only to the patients. During the induction of anesthesia, remimazolam was administered at a rate of 6 mg · kg-1 · h-1, and propofol was administered at a target effect-site concentration of 3.5 μg/ml. The electroencephalogram signals from eight channels (Fp1, Fp2, Fz, F3, F4, Pz, P3, and P4, referenced to A2, using the 10 to 20 system) were acquired during the induction of anesthesia and in the postoperative care unit. Power spectrum analysis was performed, and directed functional connectivity between frontal and parietal regions was evaluated using normalized symbolic transfer entropy. Functional connectivity in unconscious processes induced by remimazolam or propofol was compared with baseline. To compare each power of frequency over time of the two hypnotic agents, a permutation test with t statistic was conducted.

RESULTS

Compared to the baseline in the alpha band, the feedback connectivity decreased by averages of 46% and 43%, respectively, after the loss of consciousness induced by remimazolam and propofol (95% CI for the mean difference: -0.073 to -0.044 for remimazolam [P < 0.001] and -0.068 to -0.042 for propofol [P < 0.001]). Asymmetry in the feedback and feedforward connectivity in the alpha band was suppressed after the loss of consciousness induced by remimazolam and propofol. There were no significant differences in the power of each frequency over time between the two hypnotic agents (minimum q value = 0.4235).

CONCLUSIONS

Both regimens showed a greater decrease in feedback connectivity compared to a decrease in feedforward connectivity after loss of consciousness, leading to a disruption of asymmetry between the frontoparietal connectivity.

EDITOR’S PERSPECTIVE

Comparison of power spectra from overnight electroencephalography between patients with Down syndrome and matched control subjects

Electroencephalograms can capture brain oscillatory activities during sleep as a form of electrophysiological signals. We analysed electroencephalogram recordings from full-night in-laboratory polysomnography from 100 patients with Down syndrome, and 100 age- and sex-matched controls. The ages of patients with Down syndrome spanned 1 month to 31 years (median 4.4 years); 84 were younger than 12 years, and 54 were male. From each electroencephalogram, we extracted relative power in six frequency bands or rhythms (delta, theta, alpha, slow sigma, fast sigma, and beta) from six channels (frontal F3 and F4, central C3 and C4, and occipital O1 and O2) during five sleep stages (N3, N2, N1, R and W)-180 features in all. We examined differences in relative power between Down syndrome and control electroencephalograms for each feature separately. During wake and N1 sleep stages, alpha rhythms (8.0-10.5 Hz) had significantly lower power in patients with Down syndrome than controls. Moreover, the rate of increase in alpha power with age during rapid eye movement sleep was significantly slower in Down syndrome than control subjects. During wake and N1 sleep, delta rhythms (0.25-4.5 Hz) had higher power in patients with Down syndrome than controls. During N2 sleep, slow sigma rhythms (10.5-12.5 Hz) had lower power in patients with DS than controls. These findings extend previous research from routine electroencephalogram studies demonstrating that patients with Down syndrome had reduced circadian amplitude-the difference between wake alpha power and deep sleep delta power was smaller in Down syndrome than control subjects. We envision that these brain oscillatory activities may be used as surrogate markers for clinical trials for patients with Down syndrome.

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.

Horizontal cortical connections shape intrinsic traveling waves into feature-selective motifs that regulate perceptual sensitivity

Intrinsic cortical activity forms traveling waves that modulate sensory-evoked responses and perceptual sensitivity. These intrinsic traveling waves (iTWs) may arise from the coordination of synaptic activity through long-range feature-dependent horizontal connectivity within cortical areas. In a spiking network model that incorporates feature-selective patchy connections, we observe iTW motifs that result from shifts in excitatory/inhibitory balance as action potentials traverse these patchy connections. To test whether feature-selective motifs occur in vivo, we examined data recorded in the middle temporal visual area (Area MT) of marmosets performing a visual detection task. We find that some iTWs form motifs that are feature selective, exhibiting direction-selective modulations in spiking activity. Further, motifs modulate the gain of target-evoked responses and perceptual sensitivity if the target matches the preference of the motif. These results suggest that iTWs are shaped by the patchy horizontal fiber projections in the cortex and can regulate neural and perceptual sensitivity in a feature-selective manner.

Imbalance in positive and negative acceleration ratio of alpha oscillation in patients with complex regional pain syndrome

Objectives

To elucidate the functional characteristics of the brain in the presence of chronic pain using electroencephalography (EEG), with a focus on the dynamics of neural excitation and inhibition.

Methods

Resting-state EEG was performed in: 17 patients with complex regional pain syndrome (CRPS) who exhibited chronic pain higher than 20 on the visual analogue scale (VAS), 6 patients with reduced CRPS symptoms and chronic pain less than 20 on VAS, and healthy age-matched controls. For the analysis, 50 s of electroencephalogram (EEG) signals were extracted from EEG recordings during wakefulness and rest with eyes closed. The envelope of the alpha frequency band was calculated by examining the positive and negative accelerations of the envelope oscillation, ratio of positive (Ap) to negative (An) accelerations (Ap-An ratio), and mean amplitude of the envelope. Comparisons were made between patients and controls, and correlations between these EEG measures and the subjective pain VAS were evaluated.Significant differences in the value of Ap, An and Ap-An ratio were observed at temporal and central electrodes between patients with pain symptoms and controls. Those with reduced CRPS symptoms exhibited a distinct Ap-An ratio at the majority of electrodes when compared with those exhibiting chronic pain.

Conclusions

Distinct patterns in alpha wave envelope dynamics, reflecting excitatory and inhibitory activities, were associated with chronic pain in patients with CRPS. The pain-relieved state of CRPS suggested that a new balance of activities was established. This relationship indicated a potential association between altered alpha oscillation characteristics and the subjective experience of pain.

Significance

This study introduces a novel method for analyzing alpha oscillation envelopes, providing new insights into the neural pathophysiology of chronic pain in CRPS patients. This approach has the potential to enhance our understanding of the alterations in brain function that occur under chronic pain conditions.

EEG spectral attractors identify a geometric core of brain dynamics

Multidimensional reconstruction of brain attractors from electroencephalography (EEG) data enables the analysis of geometric complexity and interactions between signals in state space. Utilizing resting-state data from young and older adults, we characterize periodic (traditional frequency bands) and aperiodic (broadband exponent) attractors according to their geometric complexity and shared dynamical signatures, which we refer to as a geometric cross-parameter coupling. Alpha and aperiodic attractors are the least complex, and their global shapes are shared among all other frequency bands, affording alpha and aperiodic greater predictive power. Older adults show lower geometric complexity but greater coupling, resulting from dedifferentiation of gamma activity. The form and content of resting-state thoughts were further associated with the complexity of attractor dynamics. These findings support a process-developmental perspective on the brain's dynamic core, whereby more complex information differentiates out of an integrative and global geometric core.

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.

Optimizing real-time phase detection in diverse rhythmic biological signals for phase-specific neuromodulation

Closed-loop, phase-specific neurostimulation is a powerful method to modulate ongoing brain activity for clinical and research applications. Phase-specific stimulation relies on estimating the phase of an ongoing oscillation in real time and issuing a control command at a target phase. Phase detection algorithms based on Fast Fourier transform (FFT) are widely used due to their computational efficiency and robustness. However, it is unclear how algorithm performance depends on the spectral properties of the input signal and how algorithm parameters can be optimized. We used offline simulation to evaluate the performance of three algorithms (endpoint-corrected Hilbert Transform, Hilbert Transform and phase mapping) on three rhythmic biological signals with distinct spectral properties (rodent hippocampal theta potential, human EEG alpha and human essential tremor). First, we found that algorithm performance was more strongly influenced by signal amplitude and frequency variation compared with signal to noise ratio. Second, our simulations showed that the size of the data window for phase estimation was critical for the performance of FFT-based algorithms, where the optimal data window corresponds to the period of the oscillation. We validated this prediction with real time phase detection of hippocampal theta oscillations in freely behaving rats performing spatial navigation. Our findings define the relationship between signal properties and algorithm performance and provide a convenient method for optimizing FFT-based phase detection algorithms.

Generalized Slowing of Resting-State Neural Oscillations in People With Schizophrenia

BACKGROUND

Recent interest in how neural oscillations reflect the flow of information through the brain has led to partitioning electroencephalography (EEG) recordings into periodic (i.e., oscillatory) and aperiodic (i.e., non-oscillatory) components. While both contribute to conventional measures of power within the frequencies that compose EEG recordings, the periodic aspect characterizes true oscillations, the speed of which is thought to be critical to efficient functioning of neural systems. Given evidence of EEG power abnormalities in schizophrenia (SCZ), we sought to determine whether the periodic aspect of EEG was aberrant in people with SCZ and could serve as a general measure of brain efficiency.

METHODS

Resting-state EEGs were gathered from 104 participants with SCZ and 105 healthy control participants. We used the FOOOF toolbox to remove aperiodic neural activity. We computed the cross-correlation between power spectra for individual participants and the mean power spectrum for all participants to quantify the relative speed of neural oscillations.

RESULTS

Periodic activity in SCZ was shifted toward lower frequencies than control participants during eyes-closed rest. On average, participants with SCZ had a 0.55-Hz shift toward oscillatory slowing across the frequency spectrum that predicted worse perceptual reasoning.

CONCLUSIONS

Slowed periodic activity at rest is evident in SCZ and may represent inefficient functioning of neural circuits as reflected in worse perceptual reasoning. A slower pace of neural oscillations may be a general limitation on the transmission of information within the brain.

Macroscale traveling waves evoked by single-pulse stimulation of the human brain

Understanding the spatiotemporal dynamics of neural signal propagation is fundamental to unraveling the complexities of brain function. Emerging evidence suggests that cortico-cortical evoked potentials (CCEPs) resulting from single-pulse electrical stimulation may be used to characterize the patterns of information flow between and within brain networks. At present, the basic spatiotemporal dynamics of CCEP propagation cortically and subcortically are incompletely understood. We hypothesized that single-pulse electrical stimulation evokes neural traveling waves detectable in the three-dimensional space sampled by intracranial stereoelectroencephalography. Across a cohort of 21 adult patients with intractable epilepsy, we delivered 17,631 stimulation pulses and recorded CCEP responses in 1,019 electrode contacts. The distance between each pair of electrode contacts was approximated using three different metrics (Euclidean distance, path length, and geodesic distance), representing direct, tractographic, and transcortical propagation, respectively. For each robust CCEP, we extracted amplitude-, spectral-, and phase-based features to identify traveling waves emanating from the site of stimulation. Many evoked responses to stimulation appear to propagate as traveling waves (~14-28%), despite sparse sampling throughout the brain. These stimulation-evoked traveling waves exhibited biologically plausible propagation velocities (range 0.1-9.6 m/s). Our results reveal that direct electrical stimulation elicits neural activity with variable spatiotemporal dynamics, including the initiation of neural traveling waves.

The independence and predictivity of resting pain-free slow alpha frequency as a biomarker of pain: A reply to Mazaheri et al

In response to Mazaheri et al.'s critique, we revisited our study (Valentini et al., 2022) on the relationship between peak alpha frequency (PAF) and pain. Their commentary prompted us to reassess our data to address the independence between slow and slowing alpha brain oscillations, as well as the predictivity of slow alpha oscillations in pain perception. Bayesian correlation analyses revealed mixed support for independence. Investigating predictivity, we found inconsistent associations between pre-PAF and unpleasantness ratings. We critically reflected on methodological and theoretical issues on the path to PAF validation as a pain biomarker. We emphasized the need for diversified methodology and analytical approaches as well as robust findings across research groups.

Reactivating cue approached positive personality traits during sleep promotes positive self-referential processing

People preferentially endorse positive personality traits as more self-descriptive than negative ones, a positivity self-referential bias. Here, we investigated how to enhance positive self-referential processing, integrating wakeful cue-approach training task (CAT) and sleep-based targeted memory reactivation (TMR). In the CAT, participants gave speeded motor responses to cued positive personality traits. In a subsequent nap, we unobtrusively re-played half of the trained positive traits during slow-wave sleep (TMR). Upon awakening, CAT+TMR facilitated participants' speed in endorsing positive traits in immediate tests, and rendered participants endorse more positive traits as self-descriptive after one week. Notably, these enhancements were associated with the directionality of cue-related 1-4 Hz slow traveling waves (STW) that propagate across brain regions. Specifically, anterior-to-posterior backward STW was positively associated with these benefits, whereas forward STW showed negative associations. These findings demonstrate the potential benefits of integrated wakeful cue-approach training and sleep-based memory reactivation in strengthening positive self-referential processing.

The role of alpha oscillations in free- and goal-directed semantic associations

Alpha oscillations are known to play a central role in several higher-order cognitive functions, especially selective attention, working memory, semantic memory, and creative thinking. Nonetheless, we still know very little about the role of alpha in the generation of more remote semantic associations, which is key to creative and semantic cognition. Furthermore, it remains unclear how these oscillations are shaped by the intention to "be creative," which is the case in most creativity tasks. We aimed to address these gaps in two experiments. In Experiment 1, we compared alpha oscillatory activity (using a method which distinguishes genuine oscillatory activity from transient events) during the generation of free associations which were more vs. less distant from a given concept. In Experiment 2, we replicated these findings and also compared alpha oscillatory activity when people were generating free associations versus associations with the instruction to be creative (i.e. goal-directed). We found that alpha was consistently higher during the generation of more distant semantic associations, in both experiments. This effect was widespread, involving areas in both left and right hemispheres. Importantly, the instruction to be creative seems to increase alpha phase synchronisation from left to right temporal brain areas, suggesting that intention to be creative changed the flux of information in the brain, likely reflecting an increase in top-down control of semantic search processes. We conclude that goal-directed generation of remote associations relies on top-down mechanisms compared to when associations are freely generated.

State-Dependent tACS Effects Reveal the Potential Causal Role of Prestimulus Alpha Traveling Waves in Visual Contrast Detection

The intricate relationship between prestimulus alpha oscillations and visual contrast detection variability has been the focus of numerous studies. However, the causal impact of prestimulus alpha traveling waves on visual contrast detection remains largely unexplored. In our research, we sought to discern the causal link between prestimulus alpha traveling waves and visual contrast detection across different levels of mental fatigue. Using electroencephalography alongside a visual detection task with 30 healthy adults (13 females; 17 males), we identified a robust negative correlation between prestimulus alpha forward traveling waves (FTWs) and visual contrast threshold (VCT). Inspired by this correlation, we utilized 45/-45° phase-shifted transcranial alternating current stimulation (tACS) in a sham-controlled, double-blind, within-subject experiment with 33 healthy adults (23 females; 10 males) to directly modulate these alpha traveling waves. After the application of 45° phase-shifted tACS, we observed a substantial decrease in FTW and an increase in backward traveling waves, along with a concurrent increase in VCT, compared with the sham condition. These changes were particularly pronounced under a low fatigue state. The findings of state-dependent tACS effects reveal the potential causal role of prestimulus alpha traveling waves in visual contrast detection. Moreover, our study highlights the potential of 45/-45° phase-shifted tACS in cognitive modulation and therapeutic applications.

An Unpredictable Brain Is a Conscious, Responsive Brain

Severe traumatic brain injuries typically result in loss of consciousness or coma. In deeply comatose patients with traumatic brain injury, cortical dynamics become simple, repetitive, and predictable. We review evidence that this low-complexity, high-predictability state results from a passive cortical state, represented by a stable repetitive attractor, that hinders the flexible formation of neuronal ensembles necessary for conscious experience. Our data and those from other groups support the hypothesis that this cortical passive state is because of the loss of thalamocortical input. We identify the unpredictability and complexity of cortical dynamics captured by local field potential as a sign of recovery from this passive coma attractor. In this Perspective article, we discuss how these electrophysiological biomarkers of the recovery of consciousness could inform the design of closed-loop stimulation paradigms to treat disorders of consciousness.

Peak alpha frequency and electroencephalographic microstates are correlated with aggression in schizophrenia

Large scale retrospective studies have shown an association between schizophrenia and risk of violence. Overall, this increase in risk is small and does not justify or support stigmatizing public perceptions or media depictions of people with schizophrenia. Nonetheless, in some situations, some symptoms of schizophrenia can increase the risk of violent behavior. Prediction of this behavior would allow high impact preventive interventions. However, to date the neurobiological correlates of violent behavior in schizophrenia are not well understood, precluding the development of prognostic biomarkers. We used electroencephalography to measure alpha activity and microstates from 31 patients with schizophrenia and 18 age matched controls. Participants also completed multiple assessments of current aggressive tendencies and their lifetime history of aggressive acts. We found that individual alpha peak frequency was negatively correlated with aggression scores in both patients and controls (largest Spearman's r = -0.45). Furthermore, this result could be replicated in data taken from a single frontal channel suggesting that this may be possible to obtain in routine clinical settings (largest Spearman's r = -0.40). We also found that transitions between microstates corresponding to auditory and visual networks were inversely correlated with aggression scores. Finally, we found that, within patients, aggression was correlated with the degree of randomness between microstate transitions. This suggests that aggression is related to inappropriate switching between large scale brain networks and subsequent failure to appropriately integrate complicated environmental and internal stimuli. By elucidating some of the electrophysiological correlates of aggression, these data facilitate the development of prognostic biomarkers.

The neural oscillatory mechanism underlying human brain fingerprint recognition using a portable EEG acquisition device

In recent years, brainprint recognition has emerged as a novel method of personal identity verification. Although studies have demonstrated the feasibility of this technology, some limitations hinder its further development into the society, such as insufficient efficiency (extended wear time for multi-channel EEG cap), complex experimental paradigms (more time in learning and completing experiments), and unclear neurobiological characteristics (lack of intuitive biomarkers and an inability to eliminate the impact of noise on individual differences). Overall, these limitations are due to the incomplete understanding of the underlying neural mechanisms. Therefore, this study aims to investigate the neural mechanisms behind brainwave recognition and simplify the operation process. We recorded prefrontal resting-state EEG data from 40 participants, which is followed up over nine months using a single-channel portable brainwave device. We found that portable devices can effectively and stably capture the characteristics of different subjects in the alpha band (8-13Hz) over long periods, as well as capturing their individual differences (no alpha peak, 1 alpha peak, or 2 alpha peaks). Through correlation analysis, alpha-band activity can reveal the uniqueness of the subjects compared to others within one minute. We further used a descriptive model to dissect the oscillatory and non-oscillatory components in the alpha band, demonstrating the different contributions of fine oscillatory features to individual differences (especially amplitude and bandwidth). Our study validated the feasibility of portable brainwave devices in brainwave recognition and the underlying neural oscillation mechanisms. The fine characteristics of various alpha oscillations will contribute to the accuracy of brainwave recognition, providing new insights for the development of future brainwave recognition technology.

Differential cortical layer engagement during seizure initiation and spread in humans

Despite decades of research, we still do not understand how spontaneous human seizures start and spread - especially at the level of neuronal microcircuits. In this study, we used laminar arrays of micro-electrodes to simultaneously record the local field potentials and multi-unit neural activities across the six layers of the neocortex during focal seizures in humans. We found that, within the ictal onset zone, the discharges generated during a seizure consisted of current sinks and sources only within the infra-granular and granular layers. Outside of the seizure onset zone, ictal discharges reflected current flow in the supra-granular layers. Interestingly, these patterns of current flow evolved during the course of the seizure - especially outside the seizure onset zone where superficial sinks and sources extended into the deeper layers. Based on these observations, a framework describing cortical-cortical dynamics of seizures is proposed with implications for seizure localization, surgical targeting, and neuromodulation techniques to block the generation and propagation of seizures.

Analysis of the alpha activity envelope in electroencephalography in relation to the ratio of excitatory to inhibitory neural activity

Alpha waves, one of the major components of resting and awake cortical activity in human electroencephalography (EEG), are known to show waxing and waning, but this phenomenon has rarely been analyzed. In the present study, we analyzed this phenomenon from the viewpoint of excitation and inhibition. The alpha wave envelope was subjected to secondary differentiation. This gave the positive (acceleration positive, Ap) and negative (acceleration negative, An) values of acceleration and their ratio (Ap-An ratio) at each sampling point of the envelope signals for 60 seconds. This analysis was performed on 36 participants with Alzheimer's disease (AD), 23 with frontotemporal dementia (FTD) and 29 age-matched healthy participants (NC) whose data were provided as open datasets. The mean values of the Ap-An ratio for 60 seconds at each EEG electrode were compared between the NC and AD/FTD groups. The AD (1.41 ±0.01 (SD)) and FTD (1.40 ±0.02) groups showed a larger Ap-An ratio than the NC group (1.38 ±0.02, p<0.05). A significant correlation between the envelope amplitude of alpha activity and the Ap-An ratio was observed at most electrodes in the NC group (Pearson's correlation coefficient, r = -0.92 ±0.15, mean for all electrodes), whereas the correlation was disrupted in AD (-0.09 ±0.21, p<0.05) and disrupted in the frontal region in the FTD group. The present method analyzed the envelope of alpha waves from a new perspective, that of excitation and inhibition, and it could detect properties of the EEG, Ap-An ratio, that have not been revealed by existing methods. The present study proposed a new method to analyze the alpha activity envelope in electroencephalography, which could be related to excitatory and inhibitory neural activity.

Functional connectivity changes in meditators and novices during yoga nidra practice

Yoga nidra (YN) practice aims to induce a deeply relaxed state akin to sleep while maintaining heightened awareness. Despite the growing interest in its clinical applications, a comprehensive understanding of the underlying neural correlates of the practice of YN remains largely unexplored. In this fMRI investigation, we aim to discover the differences between wakeful resting states and states attained during YN practice. The study included individuals experienced in meditation and/or yogic practices, referred to as 'meditators' (n = 30), and novice controls (n = 31). The GLM analysis, based on audio instructions, demonstrated activation related to auditory cues without concurrent default mode network (DMN) deactivation. DMN seed based functional connectivity (FC) analysis revealed significant reductions in connectivity among meditators during YN as compared to controls. We did not find differences between the two groups during the pre and post resting state scans. Moreover, when DMN-FC was compared between the YN state and resting state, meditators showed distinct decoupling, whereas controls showed increased DMN-FC. Finally, participants exhibit a remarkable correlation between reduced DMN connectivity during YN and self-reported hours of cumulative meditation and yoga practice. Together, these results suggest a unique neural modulation of the DMN in meditators during YN which results in being restful yet aware, aligned with their subjective experience of the practice. The study deepens our understanding of the neural mechanisms of YN, revealing distinct DMN connectivity decoupling in meditators and its relationship with meditation and yoga experience. These findings have interdisciplinary implications for neuroscience, psychology, and yogic disciplines.