Alpha oscillations and traveling waves: Signatures of predictive coding?

Predictable motion is progressively extrapolated across temporally distinct processing stages in the human visual cortex

Neural processing of sensory information takes time. Consequently, to estimate the current state of the world, the brain must rely on predictive processes-for example, extrapolating the motion of a ball to determine its probable present position. Some evidence implicates early (pre-cortical) processing in extrapolation, but it remains unclear whether extrapolation continues during later-stage (cortical) processing, where further delays accumulate. Moreover, the majority of such evidence relies on invasive neurophysiological techniques in animals, with accurate characterization of extrapolation effects in the human brain currently lacking. Here, we address these issues by demonstrating how precise probabilistic maps can be constructed from human EEG recordings. Participants (N = 18, two sessions) viewed a stimulus moving along a circular trajectory while EEG was recorded. Using linear discriminant analysis (LDA) classification, we extracted maps of stimulus location over time and found evidence of a forwards temporal shift occurring across temporally distinct processing stages. This accelerated emergence of position representations indicates extrapolation occurring at multiple stages of processing, with representations progressively shifted closer to real-time. We further show evidence of representational overshoot during early-stage processing following unexpected changes to an object's trajectory, and demonstrate that the observed dynamics can emerge without supervision in a simulated neural network via spike-timing-dependent plasticity.

Fronto-occipital dyscommunication associates with brain hierarchy in schizophrenia

Schizophrenia involves abnormal fronto-occipital interactions linked to hallucinations and cognitive impairments, but the neural mechanisms remain unclear. This work aims to provide an overview of the relationship between fronto-occipital dysfunction and symptoms using simultaneous EEG-fMRI data in schizophrenia. We measured the brain's functional separation and quantified bidirectional information transfer changes between the frontal and occipital regions. A pronounced elevation in correlation within the frontal lobe, accompanied by a marked reduction in the occipital lobe, was observed between gradient eccentricities and theta-power of forward waves. Moreover, the relationship between forward waves and gradient eccentricities in the ventrolateral prefrontal cortex may be shaped by positive symptoms, while the influence of negative symptoms appears to modulate the relationship between backward waves and gradient eccentricities in the insula. The MOR and CB1 neurotransmitters predominantly contributed to associations between eccentricities and traveling waves. Symptoms promote the dysregulation of hierarchical separation and information transmission in schizophrenia.

Electroencephalography Alpha Traveling Waves as Early Predictors of Treatment Response in Major Depressive Episodes: Insights from Intermittent Photic Stimulation

Background: Early evaluation of treatment efficacy in adolescents and young adults with major depressive episodes (MDEs) remains a clinical challenge, often delaying timely therapeutic adjustments. Electroencephalography (EEG) alpha traveling waves, particularly those elicited by intermittent photic stimulation (IPS), may serve as biomarkers reflecting neural dynamics. This study aimed to investigate whether IPS-induced alpha traveling waves could predict early treatment outcomes in transitional-aged youth with MDEs. Methods: We recorded EEG signals from 119 patients aged 16-24 years at admission, prior to a standardized two-week treatment regimen. IPS was applied using multiple stimulus frequencies, and alpha traveling waves were analyzed in terms of directionality (forward vs. backward) and hemispheric lateralization. Results: Alpha traveling wave amplitudes varied across individuals, depending on stimulus frequency and hemisphere. Notably, a higher amplitude of backward alpha traveling waves at 10 Hz IPS in the left hemisphere significantly predicted positive early treatment response. In contrast, forward waves and right hemisphere responses did not show predictive value. Conclusions: IPS-induced backward alpha traveling waves in the left hemisphere may represent promising EEG biomarkers for early prediction of treatment efficacy in youth with MDEs. These findings offer a potential neurophysiological tool to support personalized treatment strategies and inform future clinical applications in adolescent and young adult depression.

An active inference account of stuttering behavior

This paper presents an interpretation of stuttering behavior, based on the principles of the active inference framework. Stuttering is a neurodevelopmental disorder characterized by speech disfluencies such as repetitions, prolongations, and blocks. The principles of active inference, a theory of predictive processing and sentient behavior, can be used to conceptualize stuttering as a disruption in perception-action cycling underlying speech production. The theory proposed here posits that stuttering arises from aberrant sensory precision and prediction error dynamics, inhibiting syllable initiation. Relevant to this theory, two hypothesized mechanisms are proposed: (1) a mistiming in precision dynamics, and (2) excessive attentional focus. Both highlight the role of neural oscillations, prediction error, and hierarchical integration in speech production. This framework also explains the contextual variability of stuttering behaviors, including adaptation effects and fluency-inducing conditions. Reframing stuttering as a synaptopathy integrates neurobiological, psychological, and behavioral dimensions, suggesting disruptions in precision-weighting mediated by neuromodulatory systems. This active inference perspective provides a unified account of stuttering and sets the stage for innovative research and therapeutic approaches.

Traveling waves in the human visual cortex: An MEG-EEG model-based approach

Brain oscillations might be traveling waves propagating in cortex. Studying their propagation within single cortical areas has mostly been restricted to invasive measurements. Their investigation in healthy humans, however, requires non-invasive recordings, such as MEG or EEG. Identifying traveling waves with these techniques is challenging because source summation, volume conduction, and low signal-to-noise ratios make it difficult to localize cortical activity from sensor responses. The difficulty is compounded by the lack of a known ground truth in traveling wave experiments. Rather than source-localizing cortical responses from sensor activity, we developed a two-part model-based neuroimaging approach: (1) The putative neural sources of a propagating oscillation were modeled within primary visual cortex (V1) via retinotopic mapping from functional MRI recordings (encoding model); and (2) the modeled sources were projected onto MEG and EEG sensors to predict the resulting signal using a biophysical head model. We tested our model by comparing its predictions against the MEG-EEG signal obtained when participants viewed visual stimuli designed to elicit either fovea-to-periphery or periphery-to-fovea traveling waves or standing waves in V1, in which ground truth cortical waves could be reasonably assumed. Correlations on within-sensor phase and amplitude relations between predicted and measured data revealed good model performance. Crucially, the model predicted sensor data more accurately when the input to the model was a traveling wave going in the stimulus direction compared to when the input was a standing wave, or a traveling wave in a different direction. Furthermore, model accuracy peaked at the spatial and temporal frequency parameters of the visual stimulation. Together, our model successfully recovers traveling wave properties in cortex when they are induced by traveling waves in stimuli. This provides a sound basis for using MEG-EEG to study endogenous traveling waves in cortex and test hypotheses related with their role in cognition.

Top-down and bottom-up interactions rely on nested brain oscillations to shape rhythmic visual attention sampling

Adaptive visual processing is enabled through the dynamic interplay between top-down and bottom-up (feedback/feedforward) information exchange, presumably propagated through brain oscillations. Here, we causally tested for the oscillatory mechanisms governing this interaction in the human visual system. Using concurrent transcranial magnetic stimulation-electroencephalography (TMS-EEG), we emulated top-down signals by a single TMS pulse over the frontal eye field (right FEF), while manipulating the strength of sensory input through the presentation of moving concentric gratings (compared to a control-TMS site). FEF-TMS without sensory input led to a top-down modulated occipital phase realignment, alongside higher fronto-occipital phase connectivity, in the alpha/beta band. Sensory input in the absence of FEF-TMS increased occipital gamma activity. Crucially, testing the interaction between top-down and bottom-up processes (FEF-TMS during sensory input) revealed an increased nesting of the bottom-up gamma activity in the alpha/beta-band cycles. This establishes a causal link between phase-to-power coupling and top-down modulation of feedforward signals, providing novel mechanistic insights into how attention interacts with sensory input at the neural level, shaping rhythmic sampling.

Sub-Second Fluctuation between Top-Down and Bottom-Up Modes Distinguishes Diverse Human Brain States

Information continuously flows between regions of the human brain, exhibiting distinct patterns that dynamically shift across states of consciousness, cognitive modes, and neuropsychiatric conditions. In this study, we introduce Relative Phase Analysis (RPA), a method that leverages phase-lead/lag relationships to reveal the real-time dynamics of dominant directional patterns and their rapid transitions. We demonstrate that the human brain switches on a sub-second timescale between a top-down mode-where anterior regions drive posterior activity-and a bottom-up mode, characterized by reverse directionality. These dynamics are most pronounced during full consciousness and gradually become less distinct as awareness diminishes. Furthermore, we find from simultaneous EEG-fMRI recordings that the top-down mode is expressed when higher-order cognitive networks are more active while the bottom-up mode is expressed when sensory systems are more active. Moreover, comparisons of an attention deficit hyperactivity disorder (ADHD) inattentive cohort with typically developing individuals reveal distinct imbalances in these transition dynamics, highlighting the potential of RPA as a diagnostic biomarker. Complementing our empirical findings, a coupled-oscillator model of the structural brain network recapitulates these emergent patterns, suggesting that such directional modes and transitions may arise naturally from inter-regional neural interactions. Altogether, this study provides a framework for understanding whole-brain dynamics in real-time and identifies sub-second fluctuations in top-down versus bottom-up directionality as a fundamental mechanism underlying human information processing.

Recent, but not long-term, priors induce behavioral oscillations in peri-saccadic vision

Perception of a continuous world relies on our ability to integrate discontinuous sensory signals when we make saccadic eye movements, which abruptly change the retinal image. Here we investigate the role of oscillations in integrating pre-saccadic information with the current sensory signals. We presented to participants (N = 24) a brief pre-saccadic Gabor stimulus (termed the inducer) before voluntary 16° saccades, followed by a test Gabor stimulus at various times before or after saccadic onset. Orientation judgments of the test stimulus were biased towards the orientation of both the inducer and previous (1-back) test stimulus, consistent with serial dependence. In addition to the average bias, judgments oscillated in synchrony with saccadic onset at alpha frequencies (~9.5 Hz) towards the orientation of the inducer or 1-back stimulus. There was also a strong bias towards the mean orientation (central tendency): however, that bias was constant over time, not associated with saccade-synched oscillations. Perceptual oscillations in serial dependence (but not central tendency) suggest that alpha rhythms may be instrumental in communicating short-term (but not long-term) perceptual memory across saccades, helping to preserve stability during saccades. The distinction between the modes of communicating short- and long-term memory suggests that the two phenomena are mediated by distinct neuronal circuitry.

Characteristics of spontaneous anterior-posterior oscillation-frequency convergences in the alpha band

Anterior-posterior interactions in the alpha band (8-12 Hz) have been implicated in a variety of functions including perception, attention, and working memory. The underlying neural communication can be flexibly controlled by adjusting phase relations when activities across anterior-posterior regions oscillate at a matched frequency. We thus investigated how alpha oscillation frequencies spontaneously converged along anterior-posterior regions by tracking oscillatory EEG activity while participants rested. As more anterior-posterior regions (scalp sites) frequency-converged, the probability of additional regions joining the frequency convergence increased, and so did oscillatory synchronization at participating regions (measured as oscillatory power), suggesting that anterior-posterior frequency convergences are driven by inter-regional entrainment. Notably, frequency convergences were accompanied by two types of approximately linear phase gradients, one progressively phase-lagged in the anterior direction-the posterior-to-anterior (P-A) gradient-and the other progressively phase-lagged in the posterior direction-the anterior-to-posterior (A-P) gradient. These gradients implied traveling waves propagating in the feedforward and feedback directions, respectively. Interestingly, while in natural viewing frequency convergences were accompanied by both gradient types (occurring at different frequencies) regardless of anterior-posterior routes, when the eyes were closed, the P-A and A-P gradients spatially segregated, channeling feedforward flows of information primarily through the midline and feedback flows primarily through each hemisphere. Future research may investigate how eye closure organizes information flows in this way and how it influences hierarchical information processing. Future research may also investigate the functional roles of frequency-convergence contingent traveling waves in contrast to those generated by other mechanisms.Significance Statement Anterior-posterior interactions in the alpha band (8-12 Hz) have been implicated in a variety of functions including perception, attention, and working memory. While alpha frequencies differ across anterior-posterior regions, they also dynamically converge while people rest. Our EEG study investigated the mechanisms and functions of spontaneous alpha-frequency convergences. Our results suggest that anterior-posterior frequency convergences are driven by inter-regional entrainment. Notably, frequency convergences were accompanied by approximately linear posterior-to-anterior and anterior-to-posterior phase gradients, likely facilitating feedforward and feedback information flows via travelling waves. Interestingly, closing eyes spatially organized these information flows, channeling feedforward flows through the midline and feedback flows through each hemisphere. Future research may investigate the behavioral significance of these frequency-convergence contingent flows of information.

Decoding the rhythmic representation and communication of visual contents

Rhythmic neural activity is considered essential for adaptively modulating responses in the visual system. In this opinion article we posit that visual brain rhythms also serve a key function in the representation and communication of visual contents. Collating a set of recent studies that used multivariate decoding methods on rhythmic brain signals, we highlight such rhythmic content representations in visual perception, imagery, and prediction. We argue that characterizing representations across frequency bands allows researchers to elegantly disentangle content transfer in feedforward and feedback directions. We further propose that alpha dynamics are central to content-specific feedback propagation in the visual system. We conclude that considering rhythmic content codes is pivotal for understanding information coding in vision and beyond.

Action Observation With Rhythm Imagery (AORI): A Novel Paradigm to Activate Motor-Related Pattern for High-Performance Motor Decoding

OBJECTIVE

The Motor Imagery (MI) paradigm has been widely used in brain-computer interface (BCI) for device control and motor rehabilitation. However, the MI paradigm faces challenges such as comprehension difficulty and limited decoding accuracy. Therefore, we propose the Action Observation with Rhythm Imagery (AORI) as a natural paradigm to provide distinct features for high-performance decoding.

METHODS

Twenty subjects were recruited in the current study to perform the AORI task. Spectral-spatial, temporal and time-frequency analyses were conducted to investigate the AORI-activated brain pattern. Task-discriminant component analysis (TDCA) was utilized to perform multiclass motor decoding.

RESULTS

The results demonstrated distinct lateralized ERD in the alpha and beta bands, and clear lateralized steady-state movement-related rhythm (SSMRR) at the movement frequencies and their first harmonics. The activated brain areas included frontal, sensorimotor, posterior parietal, and occipital regions. Notably, the decoding accuracy reached 92.16% ± 7.61% in the four-class scenario.

CONCLUSION AND SIGNIFICANCE

We proposed the AORI paradigm, revealed the activated motor-related pattern and proved its efficacy for high-performance motor decoding. These findings provide new possibilities for designing a natural and robust BCI for motor control and motor rehabilitation.

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.

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.

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'.

Oscillatory Traveling Waves Provide Evidence for Predictive Coding Abnormalities in Schizophrenia

BACKGROUND

The computational mechanisms underlying psychiatric disorders are hotly debated. One hypothesis, grounded in the Bayesian predictive coding framework, proposes that patients with schizophrenia have abnormalities in encoding prior beliefs about the environment, resulting in abnormal sensory inference, which can explain core aspects of the psychopathology, such as some of its symptoms.

METHODS

Here, we tested this hypothesis by identifying oscillatory traveling waves as neural signatures of predictive coding. We analyzed an electroencephalography dataset comprising 146 patients with schizophrenia and 96 age-matched healthy control participants during resting states and a visual backward masking task.

RESULTS

We found that patients with schizophrenia had stronger top-down alpha-band traveling waves compared with healthy control participants during resting state, supposedly reflecting overly precise priors at higher levels of the predictive processing hierarchy. We also found stronger bottom-up alpha-band waves in patients with schizophrenia during a visual task, consistent with the notion of enhanced signaling of sensory precision errors.

CONCLUSIONS

Our results yield a novel spatial-based characterization of oscillatory dynamics in schizophrenia, considering brain rhythms as traveling waves and providing a unique framework to study the different components involved in a predictive coding scheme. All together, our findings significantly advance our understanding of the mechanisms involved in fundamental pathophysiological aspects of schizophrenia, promoting a more comprehensive and hypothesis-driven approach to psychiatric disorders.

Visual Processing by Hierarchical and Dynamic Multiplexing

The complexity of natural environments requires highly flexible mechanisms for adaptive processing of single and multiple stimuli. Neuronal oscillations could be an ideal candidate for implementing such flexibility in neural systems. Here, we present a framework for structuring attention-guided processing of complex visual scenes in humans, based on multiplexing and phase coding schemes. Importantly, we suggest that the dynamic fluctuations of excitability vary rapidly in terms of magnitude, frequency and wave-form over time, i.e., they are not necessarily sinusoidal or sustained oscillations. Different elements of single objects would be processed within a single cycle (burst) of alpha activity (7-14 Hz), allowing for the formation of coherent object representations while separating multiple objects across multiple cycles. Each element of an object would be processed separately in time-expressed as different gamma band bursts (>30 Hz)-along the alpha phase. Since the processing capacity per alpha cycle is limited, an inverse relationship between object resolution and size of attentional spotlight ensures independence of the proposed mechanism from absolute object complexity. Frequency and wave-shape of those fluctuations would depend on the nature of the object that is processed and on cognitive demands. Multiple objects would further be organized along the phase of slower fluctuations (e.g., theta), potentially driven by saccades. Complex scene processing, involving covert attention and eye movements, would therefore be associated with multiple frequency changes in the alpha and lower frequency range. This framework embraces the idea of a hierarchical organization of visual processing, independent of environmental temporal dynamics.

Transformers and cortical waves: encoders for pulling in context across time

The capabilities of transformer networks such as ChatGPT and other large language models (LLMs) have captured the world's attention. The crucial computational mechanism underlying their performance relies on transforming a complete input sequence - for example, all the words in a sentence - into a long 'encoding vector' that allows transformers to learn long-range temporal dependencies in naturalistic sequences. Specifically, 'self-attention' applied to this encoding vector enhances temporal context in transformers by computing associations between pairs of words in the input sequence. We suggest that waves of neural activity traveling across single cortical areas, or multiple regions on the whole-brain scale, could implement a similar encoding principle. By encapsulating recent input history into a single spatial pattern at each moment in time, cortical waves may enable a temporal context to be extracted from sequences of sensory inputs, the same computational principle as that used in transformers.

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.

Pain reflects the informational value of nociceptive inputs

ABSTRACT

Pain perception and its modulation are fundamental to human learning and adaptive behavior. This study investigated the hypothesis that pain perception is tied to pain's learning function. Thirty-one participants performed a threat conditioning task where certain cues were associated with a possibility of receiving a painful electric shock. The cues that signaled potential pain or safety were regularly changed, requiring participants to continually establish new associations. Using computational models, we quantified participants' pain expectations and prediction errors throughout the task and assessed their relationship with pain perception and electrophysiological responses. Our findings suggest that subjective pain perception increases with prediction error, that is, when pain was unexpected. Prediction errors were also related to physiological nociceptive responses, including the amplitude of nociceptive flexion reflex and electroencephalography markers of cortical nociceptive processing (N1-P2-evoked potential and gamma-band power). In addition, higher pain expectations were related to increased late event-related potential responses and alpha/beta decreases in amplitude during cue presentation. These results further strengthen the idea of a crucial link between pain and learning and suggest that understanding the influence of learning mechanisms in pain modulation could help us understand when and why pain perception is modulated in health and disease.

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

Learning probability distributions of sensory inputs with Monte Carlo predictive coding

It has been suggested that the brain employs probabilistic generative models to optimally interpret sensory information. This hypothesis has been formalised in distinct frameworks, focusing on explaining separate phenomena. On one hand, classic predictive coding theory proposed how the probabilistic models can be learned by networks of neurons employing local synaptic plasticity. On the other hand, neural sampling theories have demonstrated how stochastic dynamics enable neural circuits to represent the posterior distributions of latent states of the environment. These frameworks were brought together by variational filtering that introduced neural sampling to predictive coding. Here, we consider a variant of variational filtering for static inputs, to which we refer as Monte Carlo predictive coding (MCPC). We demonstrate that the integration of predictive coding with neural sampling results in a neural network that learns precise generative models using local computation and plasticity. The neural dynamics of MCPC infer the posterior distributions of the latent states in the presence of sensory inputs, and can generate likely inputs in their absence. Furthermore, MCPC captures the experimental observations on the variability of neural activity during perceptual tasks. By combining predictive coding and neural sampling, MCPC can account for both sets of neural data that previously had been explained by these individual frameworks.

Effective Connectivity Network of Aberrant Prediction Error Processing in Auditory Phantom Perception

Introduction: Prediction error (PE) is key to perception in the predictive coding framework. However, previous studies indicated the varied neural activities evoked by PE in tinnitus patients. Here, we aimed to reconcile the conflict by (1) a more nuanced view of PE, which could be driven by changing stimulus (stimulus-driven PE [sPE]) and violation of current context (context-driven PE [cPE]) and (2) investigating the aberrant connectivity networks that are engaged in the processing of the two types of PEs in tinnitus patients. Methods: Ten tinnitus patients with normal hearing and healthy controls were recruited, and a local-global auditory oddball paradigm was applied to measure the electroencephalographic difference between the two groups during sPE and cPE conditions. Results: Overall, the sPE condition engaged bottom-up and top-down connections, whereas the cPE condition engaged mostly top-down connections. The tinnitus group showed decreased sensitivity to the sPE and increased sensitivity to the cPE condition. Particularly, the auditory cortex and posterior cingulate cortex were the hubs for processing cPE in the control and tinnitus groups, respectively, showing the orientation to an internal state in tinnitus. Furthermore, tinnitus patients showed stronger connectivity to the parahippocampus and pregenual anterior cingulate cortex for the establishment of the prediction during the cPE condition. Conclusion: These results begin to dissect the role of changes in stimulus characteristics versus changes in the context of processing the same stimulus in mechanisms of tinnitus generation. Impact Statement This study delves into the number dynamics of prediction error (PE) in tinnitus, proposing a dual framework distinguishing between stimulus-driven PE (sPE) and context-driven PE (cPE). Electroencephalographic data from tinnitus patients and controls revealed distinct connectivity patterns during sPE and cPE conditions. Tinnitus patients exhibited reduced sensitivity to sPE and increased sensitivity to cPE. The auditory cortex and posterior cingulate cortex emerged as pivotal regions for cPE processing in controls and tinnitus patients, indicative of an internal state orientation in tinnitus. Enhanced connectivity to the parahippocampus and pregenual anterior cingulate cortex underscores the role of context in tinnitus pathophysiology.

Neurobiological research on N,N-dimethyltryptamine (DMT) and its potentiation by monoamine oxidase (MAO) inhibition: from ayahuasca to synthetic combinations of DMT and MAO inhibitors

The potent hallucinogen N,N-dimethyltryptamine (DMT) has garnered significant interest in recent years due to its profound effects on consciousness and its therapeutic psychopotential. DMT is an integral (but not exclusive) psychoactive alkaloid in the Amazonian plant-based brew ayahuasca, in which admixture of several β-carboline monoamine oxidase A (MAO-A) inhibitors potentiate the activity of oral DMT, while possibly contributing in other respects to the complex psychopharmacology of ayahuasca. Irrespective of the route of administration, DMT alters perception, mood, and cognition, presumably through agonism at serotonin (5-HT) 1A/2A/2C receptors in brain, with additional actions at other receptor types possibly contributing to its overall psychoactive effects. Due to rapid first pass metabolism, DMT is nearly inactive orally, but co-administration with β-carbolines or synthetic MAO-A inhibitors (MAOIs) greatly increase its bioavailability and duration of action. The synergistic effects of DMT and MAOIs in ayahuasca or synthetic formulations may promote neuroplasticity, which presumably underlies their promising therapeutic efficacy in clinical trials for neuropsychiatric disorders, including depression, addiction, and post-traumatic stress disorder. Advances in neuroimaging techniques are elucidating the neural correlates of DMT-induced altered states of consciousness, revealing alterations in brain activity, functional connectivity, and network dynamics. In this comprehensive narrative review, we present a synthesis of current knowledge on the pharmacology and neuroscience of DMT, β-carbolines, and ayahuasca, which should inform future research aiming to harness their full therapeutic potential.

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.

Phase-shifted tACS can modulate cortical alpha waves in human subjects

In the present study, we investigated traveling waves induced by transcranial alternating current stimulation in the alpha frequency band of healthy subjects. Electroencephalographic data were recorded in 12 healthy subjects before, during, and after phase-shifted stimulation with a device combining both electroencephalographic and stimulation capacities. In addition, we analyzed the results of numerical simulations and compared them to the results of identical analysis on real EEG data. The results of numerical simulations indicate that imposed transcranial alternating current stimulation induces a rotating electric field. The direction of waves induced by stimulation was observed more often during at least 30 s after the end of stimulation, demonstrating the presence of aftereffects of the stimulation. Results suggest that the proposed approach could be used to modulate the interaction between distant areas of the cortex. Non-invasive transcranial alternating current stimulation can be used to facilitate the propagation of circulating waves at a particular frequency and in a controlled direction. The results presented open new opportunities for developing innovative and personalized transcranial alternating current stimulation protocols to treat various neurological disorders.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-023-09997-1.

An Integrated theory of false insights and beliefs under psychedelics

Psychedelics are recognised for their potential to re-orient beliefs. We propose a model of how psychedelics can, in some cases, lead to false insights and thus false beliefs. We first review experimental work on laboratory-based false insights and false memories. We then connect this to insights and belief formation under psychedelics using the active inference framework. We propose that subjective and brain-based alterations caused by psychedelics increases the quantity and subjective intensity of insights and thence beliefs, including false ones. We offer directions for future research in minimising the risk of false and potentially harmful beliefs arising from psychedelics. Ultimately, knowing how psychedelics may facilitate false insights and beliefs is crucial if we are to optimally leverage their therapeutic potential.

The interplay of cognitive control and feature integration: insights from theta oscillatory dynamics during conflict processing

Adaptive behavior is fundamental to cognitive control and executive functioning. This study investigates how cognitive control mechanisms and episodic feature retrieval interact to influence adaptiveness, focusing particularly on theta (4 to 8 Hz) oscillatory dynamics. We conducted two variations of the Simon task, incorporating response-incompatible, response-compatible, and neutral trials. Experiment 1 demonstrated that cognitive adjustments-specifically, cognitive shielding following incompatible trials and cognitive relaxation following compatible ones-are reflected in midfrontal theta power modulations associated with the Simon effect. Experiment 2 showed that reducing feature overlap between trials leads to less pronounced sequential modulations in behavior and midfrontal theta activity, supporting the hypothesis that cognitive control and feature integration share a common neural mechanism. These findings highlight the interaction of cognitive control processes and episodic feature integration in modulating behavior. The results advocate for hybrid models that combine top-down and bottom-up processes as a comprehensive framework to understand cognitive control dynamics and adaptive behavior.

Dynamic Formation of a Posterior-to-Anterior Peak-Alpha-Frequency Gradient Driven by Two Distinct Processes

Peak-alpha frequency varies across individuals and mental states, but it also forms a negative gradient from posterior to anterior regions in association with increases in cortical thickness and connectivity, reflecting a cortical hierarchy in temporal integration. Tracking the spatial standard deviation of peak-alpha frequency in scalp EEG, we observed that a posterior-to-anterior gradient dynamically formed and dissolved. Periods of high spatial standard deviation yielded robustly negative posterior-to-anterior gradients-the "gradient state"-while periods of low spatial standard deviation yielded globally converged peak-alpha frequency-the "uniform state." The state variations were characterized by a combination of slow (0.3-0.5 Hz) oscillations and random-walk-like fluctuations. They were relatively independently correlated with peak-alpha frequency variations in anterior regions and peak-alpha power variations in central regions driven by posterior regions (together accounting for ∼50% of the state variations), suggesting that two distinct mechanisms modulate the state variations: an anterior mechanism that directly adjusts peak-alpha frequencies and a posterior-central mechanism that indirectly adjusts them by influencing synchronization. The state variations likely reflect general operations as their spatiotemporal characteristics remained unchanged while participants engaged in a variety of tasks (breath focus, vigilance, working memory, mental arithmetic, and generative thinking) with their eyes closed or watched a silent nature video. The ongoing state variations may dynamically balance two global processing modes, one that facilitates greater temporal integration (and potentially also information influx) toward anterior regions in the gradient state and the other that facilitates flexible global communication (via phase locking) in the uniform state.

Mixed selectivity: Cellular computations for complexity

The property of mixed selectivity has been discussed at a computational level and offers a strategy to maximize computational power by adding versatility to the functional role of each neuron. Here, we offer a biologically grounded implementational-level mechanistic explanation for mixed selectivity in neural circuits. We define pure, linear, and nonlinear mixed selectivity and discuss how these response properties can be obtained in simple neural circuits. Neurons that respond to multiple, statistically independent variables display mixed selectivity. If their activity can be expressed as a weighted sum, then they exhibit linear mixed selectivity; otherwise, they exhibit nonlinear mixed selectivity. Neural representations based on diverse nonlinear mixed selectivity are high dimensional; hence, they confer enormous flexibility to a simple downstream readout neural circuit. However, a simple neural circuit cannot possibly encode all possible mixtures of variables simultaneously, as this would require a combinatorially large number of mixed selectivity neurons. Gating mechanisms like oscillations and neuromodulation can solve this problem by dynamically selecting which variables are mixed and transmitted to the readout.

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.

Disruption of alpha oscillation propagation in patients with schizophrenia

OBJECTIVE

Propagation of electroencephalogram (EEG) oscillations, often referred to as traveling waves, reflects the role of brain oscillations in neural information transmission. This propagation can be distorted by brain disorders such as schizophrenia that features disconnection of neural information transmission (i.e., disconnection syndrome). However, this possibility of the disruption of EEG oscillation propagation in patients with schizophrenia remains largely unexplored.

METHODS

Using a publicly shared dataset (N = 19 and 24; patients with schizophrenia and healthy controls, respectively), we investigated EEG oscillation propagation by analyzing the local phase gradients (LPG) of alpha (8-12 Hz) oscillations in both healthy participants and patients with schizophrenia.

RESULTS

Our results showed significant directionality in the propagation of alpha oscillations in healthy participants. Specifically, alpha oscillations propagated in an anterior-to-posterior direction along mid-line and a posterior-to-anterior direction laterally. In patients with schizophrenia, some of alpha oscillation propagation were notably disrupted, particularly in the central midline area where alpha oscillations propagated from anterior to posterior areas.

CONCLUSION

Our finding lends support to the hypothesis of a disconnection syndrome in schizophrenia, underscoring a disruption in the anterior-to-posterior propagation of alpha oscillations.

SIGNIFICANCE

This study identified disruption of alpha oscillation propagation observed in scalp EEG as a biomarker for schizophrenia.

Perceptual Expectations Are Reflected by Early Alpha Power Reduction

The predictability of a stimulus can be characterized by its transitional probability. Perceptual expectations derived from the transitional probability of the stimulus were found to modulate the early alpha oscillations in the sensory regions of the brain when neural responses to expected versus unexpected stimuli were compared. The objective of our study was to find out the extent to which this low-frequency oscillation reflects stimulus predictability. We aimed to detect the alpha-power difference with smaller differences in transitional probabilities by comparing expected stimuli with neutral ones. We studied the effect of expectation on perception by applying an unsupervised visual statistical learning paradigm with expected and neutral stimuli embedded in an image sequence while recording EEG. Time-frequency analysis showed that expected stimuli elicit lower alpha power in the window of 8-12 Hz and 0-400 msec after stimulus presentation, appearing in the centroparietal region. Comparing previous findings of expectancy-based alpha-band modulation with our results suggests that early alpha oscillation shows an inverse relationship with stimulus predictability. Although current data are insufficient to determine the origin of the alpha power reduction, this could be a potential sign of expectation suppression in cortical oscillatory activity.

The direction of theta and alpha travelling waves modulates human memory processing

To support a range of behaviours, the brain must flexibly coordinate neural activity across widespread brain regions. One potential mechanism for this coordination is a travelling wave, in which a neural oscillation propagates across the brain while organizing the order and timing of activity across regions. Although travelling waves are present across the brain in various species, their potential functional relevance has remained unknown. Here, using rare direct human brain recordings, we demonstrate a distinct functional role for travelling waves of theta- and alpha-band (2-13 Hz) oscillations in the cortex. Travelling waves propagate in different directions during separate cognitive processes. In episodic memory, travelling waves tended to propagate in a posterior-to-anterior direction during successful memory encoding and in an anterior-to-posterior direction during recall. Because travelling waves of oscillations correspond to local neuronal spiking, these patterns indicate that rhythmic pulses of activity move across the brain in different directions for separate behaviours. More broadly, our results suggest a fundamental role for travelling waves and oscillations in dynamically coordinating neural connectivity, by flexibly organizing the timing and directionality of network interactions across the cortex to support cognition and behaviour.

Human connectome topology directs cortical traveling waves and shapes frequency gradients

Traveling waves and neural oscillation frequency gradients are pervasive in the human cortex. While the direction of traveling waves has been linked to brain function and dysfunction, the factors that determine this direction remain elusive. We hypothesized that structural connectivity instrength gradients - defined as the gradually varying sum of incoming connection strengths across the cortex - could shape both traveling wave direction and frequency gradients. We confirm the presence of instrength gradients in the human connectome across diverse cohorts and parcellations. Using a cortical network model, we demonstrate how these instrength gradients direct traveling waves and shape frequency gradients. Our model fits resting-state MEG functional connectivity best in a regime where instrength-directed traveling waves and frequency gradients emerge. We further show how structural subnetworks of the human connectome generate opposing wave directions and frequency gradients observed in the alpha and beta bands. Our findings suggest that structural connectivity instrength gradients affect both traveling wave direction and frequency gradients.

Neural assemblies coordinated by cortical waves are associated with waking and hallucinatory brain states

The relationship between sensory stimuli and perceptions is brain-state dependent: in wakefulness, suprathreshold stimuli evoke perceptions; under anesthesia, perceptions are abolished; and during dreaming and in dissociated states, percepts are internally generated. Here, we exploit this state dependence to identify brain activity associated with internally generated or stimulus-evoked perceptions. In awake mice, visual stimuli phase reset spontaneous cortical waves to elicit 3-6 Hz feedback traveling waves. These stimulus-evoked waves traverse the cortex and entrain visual and parietal neurons. Under anesthesia as well as during ketamine-induced dissociation, visual stimuli do not disrupt spontaneous waves. Uniquely, in the dissociated state, spontaneous waves traverse the cortex caudally and entrain visual and parietal neurons, akin to stimulus-evoked waves in wakefulness. Thus, coordinated neuronal assemblies orchestrated by traveling cortical waves emerge in states in which perception can manifest. The awake state is privileged in that this coordination is reliably elicited by external visual stimuli.

A Role for Bottom-Up Alpha Oscillations in Temporal Integration

Neural oscillations in the 8-12 Hz alpha band are thought to represent top-down inhibitory control and to influence temporal resolution: Individuals with faster peak frequencies segregate stimuli appearing closer in time. Recently, this theory has been challenged. Here, we investigate a special case in which alpha does not correlate with temporal resolution: when stimuli are presented amidst strong visual drive. Based on findings regarding alpha rhythmogenesis and wave spatial propagation, we suggest that stimulus-induced, bottom-up alpha oscillations play a role in temporal integration. We propose a theoretical model, informed by visual persistence, lateral inhibition, and network refractory periods, and simulate physiologically plausible scenarios of the interaction between bottom-up alpha and the temporal segregation. Our simulations reveal that different features of oscillations, including frequency, phase, and power, can influence temporal perception and provide a theoretically informed starting point for future empirical studies.

Individual Alpha Frequency Contributes to the Precision of Human Visual Processing

Brain oscillatory activity within the alpha band has been associated with a wide range of processes encompassing perception, memory, decision-making, and overall cognitive functioning. Individual alpha frequency (IAF) is a specific parameter accounting for the mean velocity of the alpha cycling activity, conventionally ranging between ∼7 and ∼13 Hz. One influential hypothesis has proposed a fundamental role of this cycling activity in the segmentation of sensory input and in the regulation of the speed of sensory processing, with faster alpha oscillations resulting in greater temporal resolution and more refined perceptual experience. However, although several recent theoretical and empirical studies would support this account, contradictory evidence suggests caution and more systematic approaches in the assessment and interpretation of this hypothesis. For example, it remains to be explored to what degree IAF shapes perceptual outcomes. In the present study, we investigated whether inter-individual differences in bias-free visual contrast detection threshold in a large sample of individuals in the general population (n = 122) could be explained by inter-individual differences in alpha pace. Our results show that the contrast needed to correctly identify target stimuli (individual perceptual threshold) is associated with alpha peak frequency (not amplitude). Specifically, individuals who require reduced contrast show higher IAF than individuals requiring higher contrasts. This suggests that inter-individual differences in alpha frequency contribute to performance variability in low-level perceptual tasks, supporting the hypothesis that IAF underlies a fundamental temporal sampling mechanism that shapes visual objective performance, with higher frequencies promoting enhanced sensory evidence per time unit.

Sensory Drive Modifies Brain Dynamics and the Temporal Integration Window

Perception is suggested to occur in discrete temporal windows, clocked by cycles of neural oscillations. An important testable prediction of this theory is that individuals' peak frequencies of oscillations should correlate with their ability to segregate the appearance of two successive stimuli. An influential study tested this prediction and showed that individual peak frequency of spontaneously occurring alpha (8-12 Hz) correlated with the temporal segregation threshold between two successive flashes of light [Samaha, J., & Postle, B. R. The speed of alpha-band oscillations predicts the temporal resolution of visual perception. Current Biology, 25, 2985-2990, 2015]. However, these findings were recently challenged [Buergers, S., & Noppeney, U. The role of alpha oscillations in temporal binding within and across the senses. Nature Human Behaviour, 6, 732-742, 2022]. To advance our understanding of the link between oscillations and temporal segregation, we devised a novel experimental approach. Rather than relying entirely on spontaneous brain dynamics, we presented a visual grating before the flash stimuli that is known to induce continuous oscillations in the gamma band (45-65 Hz). By manipulating the contrast of the grating, we found that high contrast induces a stronger gamma response and a shorter temporal segregation threshold, compared to low-contrast trials. In addition, we used a novel tool to characterize sustained oscillations and found that, for half of the participants, both the low- and high-contrast gratings were accompanied by a sustained and phase-locked alpha oscillation. These participants tended to have longer temporal segregation thresholds. Our results suggest that visual stimulus drive, reflected by oscillations in specific bands, is related to the temporal resolution of visual perception.

A Traveling Waves Perspective on Temporal Binding

Brain oscillations are involved in many cognitive processes, and several studies have investigated their role in cognition. In particular, the phase of certain oscillations has been related to temporal binding and integration processes, with some authors arguing that perception could be an inherently rhythmic process. However, previous research on oscillations mostly overlooked their spatial component: how oscillations propagate through the brain as traveling waves, with systematic phase delays between brain regions. Here, we argue that interpreting oscillations as traveling waves is a useful paradigm shift to understand their role in temporal binding and address controversial results. After a brief definition of traveling waves, we propose an original view on temporal integration that considers this new perspective. We first focus on cortical dynamics, then speculate about the role of thalamic nuclei in modulating the waves, and on the possible consequences for rhythmic temporal binding. In conclusion, we highlight the importance of considering oscillations as traveling waves when investigating their role in cognitive functions.

Alpha Oscillations and Temporal Binding Windows in Perception-A Critical Review and Best Practice Guidelines

An intriguing question in cognitive neuroscience is whether alpha oscillations shape how the brain transforms the continuous sensory inputs into distinct percepts. According to the alpha temporal resolution hypothesis, sensory signals arriving within a single alpha cycle are integrated, whereas those in separate cycles are segregated. Consequently, shorter alpha cycles should be associated with smaller temporal binding windows and higher temporal resolution. However, the evidence supporting this hypothesis is contentious, and the neural mechanisms remain unclear. In this review, we first elucidate the alpha temporal resolution hypothesis and the neural circuitries that generate alpha oscillations. We then critically evaluate study designs, experimental paradigms, psychophysics, and neurophysiological analyses that have been employed to investigate the role of alpha frequency in temporal binding. Through the lens of this methodological framework, we then review evidence from between-subject, within-subject, and causal perturbation studies. Our review highlights the inherent interpretational ambiguities posed by previous study designs and experimental paradigms and the extensive variability in analysis choices across studies. We also suggest best practice recommendations that may help to guide future research. To establish a mechanistic role of alpha frequency in temporal parsing, future research is needed that demonstrates its causal effects on the temporal binding window with consistent, experimenter-independent methods.

Brain network hypersensitivity underlies pain crises in sickle cell disease

Sickle cell disease (SCD) is a genetic disorder causing painful and unpredictable Vaso-occlusive crises (VOCs) through blood vessel blockages. In this study, we propose explosive synchronization (ES) as a novel approach to comprehend the hypersensitivity and occurrence of VOCs in the SCD brain network. We hypothesized that the accumulated disruptions in the brain network induced by SCD might lead to strengthened ES and hypersensitivity. We explored ES's relationship with patient reported outcome measures (PROMs) as well as VOCs by analyzing EEG data from 25 SCD patients and 18 matched controls. SCD patients exhibited lower alpha frequency than controls. SCD patients showed correlation between frequency disassortativity (FDA), an ES condition, and three important PROMs. Furthermore, stronger FDA was observed in SCD patients with a higher frequency of VOCs and EEG recording near VOC. We also conducted computational modeling on SCD brain network to study FDA's role in network sensitivity. Our model demonstrated that a stronger FDA could be linked to increased sensitivity and frequency of VOCs. This study establishes connections between SCD pain and the universal network mechanism, ES, offering a strong theoretical foundation. This understanding will aid predicting VOCs and refining pain management for SCD patients.

Serial Dependence in Perception

Much evidence has shown that perception is biased towards previously presented similar stimuli, an effect recently termed serial dependence. Serial dependence affects nearly every aspect of perception, often causing gross perceptual distortions, especially for weak and ambiguous stimuli. Despite unwanted side-effects, empirical evidence and Bayesian modeling show that serial dependence acts to improve efficiency and is generally beneficial to the system. Consistent with models of predictive coding, the Bayesian priors of serial dependence are generated at high levels of cortical analysis, incorporating much perceptual experience, but feed back to lower sensory areas. These feedback loops may drive oscillations in the alpha range, linked strongly with serial dependence. The discovery of top-down predictive perceptual processes is not new, but the new, more quantitative approach characterizing serial dependence promises to lead to a deeper understanding of predictive perceptual processes and their underlying neural mechanisms.

Traveling waves shape neural population dynamics enabling predictions and internal model updating

The brain generates predictions based on statistical regularities in our environment. However, it is unclear how predictions are optimized through iterative interactions with the environment. Because traveling waves (TWs) propagate across the cortex shaping neural excitability, they can carry information to serve predictive processing. Using human intracranial recordings, we show that anterior-to-posterior alpha TWs correlated with prediction strength. Learning about priors altered neural state space trajectories, and how much it altered correlated with trial-by-trial prediction strength. Learning involved mismatches between predictions and sensory evidence triggering alpha-phase resets in lateral temporal cortex, accompanied by stronger alpha phase-high gamma amplitude coupling and high-gamma power. The mismatch initiated posterior-to-anterior alpha TWs and change in the subsequent trial's state space trajectory, facilitating model updating. Our findings suggest a vital role of alpha TWs carrying both predictions to sensory cortex and mismatch signals to frontal cortex for trial-by-trial fine-tuning of predictive models.

On natural attunement: Shared rhythms between the brain and the environment

Rhythms exist both in the embodied brain and the built environment. Becoming attuned to the rhythms of the environment, such as repetitive columns, can greatly affect perception. Here, we explore how the built environment affects human cognition and behavior through the concept of natural attunement, often resulting from the coordination of a person's sensory and motor systems with the rhythmic elements of the environment. We argue that the built environment should not be reduced to mere states, representations, and single variables but instead be considered a bundle of highly related continuous signals with which we can resonate. Resonance and entrainment are dynamic processes observed when intrinsic frequencies of the oscillatory brain are influenced by the oscillations of an external signal. This allows visual rhythmic stimulations of the environment to affect the brain and body through neural entrainment, cross-frequency coupling, and phase resetting. We review how real-world architectural settings can affect neural dynamics, cognitive processes, and behavior in people, suggesting the crucial role of everyday rhythms in the brain-body-environment relationship.

Alpha-frequency feedback to early visual cortex orchestrates coherent naturalistic vision

During naturalistic vision, the brain generates coherent percepts by integrating sensory inputs scattered across the visual field. Here, we asked whether this integration process is mediated by rhythmic cortical feedback. In electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) experiments, we experimentally manipulated integrative processing by changing the spatiotemporal coherence of naturalistic videos presented across visual hemifields. Our EEG data revealed that information about incoherent videos is coded in feedforward-related gamma activity while information about coherent videos is coded in feedback-related alpha activity, indicating that integration is indeed mediated by rhythmic activity. Our fMRI data identified scene-selective cortex and human middle temporal complex (hMT) as likely sources of this feedback. Analytically combining our EEG and fMRI data further revealed that feedback-related representations in the alpha band shape the earliest stages of visual processing in cortex. Together, our findings indicate that the construction of coherent visual experiences relies on cortical feedback rhythms that fully traverse the visual hierarchy.

The role of neural oscillations in visuo-motor communication at the time of saccades

Saccadic eye-movements are fundamental for active vision, allowing observers to purposefully scan the environment with the high-resolution fovea. In this brief perspective we outline a series of experiments from our laboratories investigating the role of eye-movements and their consequences to active perception. We show that saccades lead to suppression of visual sensitivity at saccadic onset, and that this suppression is accompanied by endogenous neural oscillations in the delta range. Similar oscillations are initiated by purposeful hand movements, which lead to measurable changes in responsivity in area V1, and in the connectivity with motor area M1. Saccades also lead to clear distortions in apparent position, but only for verbal reports, not when participants respond with rapid pointing, consistent with the action of two separate visual systems in neurotypical adults. At the time of saccades, serial dependence, the positive influence on perception of previous stimulus attributes (such as orientation) is particularly strong. Again, these processes are accompanied by neural oscillations, in the alpha and low beta range. In general, oscillations seem to be tightly linked to serial dependence in perception, both in auditory judgments (around 10 Hz), and for visual judgements of face gender (14 Hz for female, 17 Hz for male). Taken together, the studies show that neural oscillations play a fundamental role in dynamic, active vision.

Storm on predictive brain: A neurocomputational account of ketamine antidepressant effect

For the past decade, ketamine, an N-methyl-D-aspartate receptor (NMDAr) antagonist, has been considered a promising treatment for major depressive disorder (MDD). Unlike the delayed effect of monoaminergic treatment, ketamine may produce fast-acting antidepressant effects hours after a single administration at subanesthetic dose. Along with these antidepressant effects, it may also induce transient dissociative (disturbing of the sense of self and reality) symptoms during acute administration which resolve within hours. To understand ketamine's rapid-acting antidepressant effect, several biological hypotheses have been explored, but despite these promising avenues, there is a lack of model to understand the timeframe of antidepressant and dissociative effects of ketamine. In this article, we propose a neurocomputational account of ketamine's antidepressant and dissociative effects based on the Predictive Processing (PP) theory, a framework for cognitive and sensory processing. PP theory suggests that the brain produces top-down predictions to process incoming sensory signals, and generates bottom-up prediction errors (PEs) which are then used to update predictions. This iterative dynamic neural process would relies on N-methyl-D-aspartate (NMDAr) and α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic receptors (AMPAr), two major component of the glutamatergic signaling. Furthermore, it has been suggested that MDD is characterized by over-rigid predictions which cannot be updated by the PEs, leading to miscalibration of hierarchical inference and self-reinforcing negative feedback loops. Based on former empirical studies using behavioral paradigms, neurophysiological recordings, and computational modeling, we suggest that ketamine impairs top-down predictions by blocking NMDA receptors, and enhances presynaptic glutamate release and PEs, producing transient dissociative symptoms and fast-acting antidepressant effect in hours following acute administration. Moreover, we present data showing that ketamine may enhance a delayed neural plasticity pathways through AMPAr potentiation, triggering a prolonged antidepressant effect up to seven days for unique administration. Taken together, the two sides of antidepressant effects with distinct timeframe could constitute the keystone of antidepressant properties of ketamine. These PP disturbances may also participate to a ketamine-induced time window of mental flexibility, which can be used to improve the psychotherapeutic process. Finally, these proposals could be used as a theoretical framework for future research into fast-acting antidepressants, and combination with existing antidepressant and psychotherapy.

Beta traveling waves in monkey frontal and parietal areas encode recent reward history

Brain function depends on neural communication, but the mechanisms of this communication are not well understood. Recent studies suggest that one form of neural communication is through traveling waves (TWs)-patterns of neural oscillations that propagate within and between brain areas. We show that TWs are robust in microarray recordings in frontal and parietal cortex and encode recent reward history. Two adult male monkeys made saccades to obtain probabilistic rewards and were sensitive to the (statistically irrelevant) reward on the previous trial. TWs in frontal and parietal areas were stronger in trials that followed a prior reward versus a lack of reward and, in the frontal lobe, correlated with the monkeys' behavioral sensitivity to the prior reward. The findings suggest that neural communication mediated by TWs within the frontal and parietal lobes contribute to maintaining information about recent reward history and mediating the impact of this history on the monkeys' expectations.

Breathing in waves: Understanding respiratory-brain coupling as a gradient of predictive oscillations

Breathing plays a crucial role in shaping perceptual and cognitive processes by regulating the strength and synchronisation of neural oscillations. Numerous studies have demonstrated that respiratory rhythms govern a wide range of behavioural effects across cognitive, affective, and perceptual domains. Additionally, respiratory-modulated brain oscillations have been observed in various mammalian models and across diverse frequency spectra. However, a comprehensive framework to elucidate these disparate phenomena remains elusive. In this review, we synthesise existing findings to propose a neural gradient of respiratory-modulated brain oscillations and examine recent computational models of neural oscillations to map this gradient onto a hierarchical cascade of precision-weighted prediction errors. By deciphering the computational mechanisms underlying respiratory control of these processes, we can potentially uncover new pathways for understanding the link between respiratory-brain coupling and psychiatric disorders.