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Predictive coding is a key mechanism to understand the computational processes underlying brain functioning: in a hierarchical network, higher levels predict the activity of lower levels, and the unexplained residuals (i.e., prediction errors) are passed back to higher layers. Because of its recursive nature, we wondered whether predictive coding could be related to brain oscillatory dynamics. First, we show that a simple 2-level predictive coding model of visual cortex, with physiological communication delays between levels, naturally gives rise to alpha-band rhythms, similar to experimental observations. Then, we demonstrate that a multilevel version of the same model can explain the occurrence of oscillatory traveling waves across levels, both forward (during visual stimulation) and backward (during rest). Remarkably, the predictions of our model are matched by the analysis of 2 independent electroencephalography (EEG) datasets, in which we observed oscillatory traveling waves in both directions.

Previous research has associated alpha-band [8–12 Hz] oscillations with inhibitory functions: for instance, several studies showed that visual attention increases alpha-band power in the hemisphere ipsilateral to the attended location. However, other studies demonstrated that alpha oscillations positively correlate with visual perception, hinting at different processes underlying their dynamics. Here, using an approach based on traveling waves, we demonstrate that there are two functionally distinct alpha-band oscillations propagating in different directions. We analyzed EEG recordings from three datasets of human participants performing a covert visual attention task (one new dataset with N = 16, two previously published datasets with N = 16 and N = 31). Participants were instructed to detect a brief target by covertly attending to the screen’s left or right side. Our analysis reveals two distinct processes: allocating attention to one hemifield increases top-down alpha-band waves propagating from frontal to occipital regions ipsilateral to the attended location, both with and without visual stimulation. These top-down oscillatory waves correlate positively with alpha-band power in frontal and occipital regions. Yet, different alpha-band waves propagate from occipital to frontal regions and contralateral to the attended location. Crucially, these forward waves were present only during visual stimulation, suggesting a separate mechanism related to visual processing. Together, these results reveal two distinct processes reflected by different propagation directions, demonstrating the importance of considering oscillations as traveling waves when characterizing their functional role.

Recent studies have shown that cortical low-frequency oscillations are often organized as traveling waves. The properties of these waves have been linked to both sensory processing and cognitive functions. In EEG recordings, alpha-band (~10Hz) traveling waves propagate predominantly along the occipital-frontal axis, with forward waves being most prominent during visual processing, while backward waves dominate at rest and during sensory suppression. While a previous study has proposed a functional model to explain their generation and propagation, a biologically plausible implementation is lacking. Here, we present a multi-scale network model with mean-field dynamics that, building on known cortical connectivity, reproduces the dynamics of alpha-band traveling waves observed in EEG recordings. We show that forward and backward waves can arise from two distinct cortical sub-networks that are connected in infragranular layers at each area. At rest, the network generates spontaneous backward waves and switches to a forward state upon sensory stimulation, reproducing the dynamics observed in EEG recordings. We then show that a cortico-thalamic pathway through the pulvinar can bias the dynamics to the forward state and that pulvinar engagement leads to spontaneous forward waves at rest. This is in line with previous studies suggesting a key role for the pulvinar in directing cortical information flow. In summary, our model provides a biologically plausible architecture for modeling the dynamics of macroscale traveling waves. It bridges the gap between scales by connecting laminar activity to scalp-level spatial patterns, providing a biologically grounded and comprehensive view of the spatial propagation of alpha-band traveling waves.

Psychedelic drugs are potent modulators of conscious states and therefore powerful tools for investigating their neurobiology. N,N, Dimethyltryptamine (DMT) can rapidly induce an extremely immersive state of consciousness characterized by vivid and elaborate visual imagery. Here, we investigated the electrophysiological correlates of the DMT-induced altered state from a pool of participants receiving DMT and (separately) placebo (saline) while instructed to keep their eyes closed. Consistent with our hypotheses, results revealed a spatio-temporal pattern of cortical activation (i.e. travelling waves) similar to that elicited by visual stimulation. Moreover, the typical top-down alpha-band rhythms of closed-eyes rest were significantly decreased, while the bottom-up forward wave was significantly increased. These results support a recent model proposing that psychedelics reduce the ‘precision-weighting of priors’, thus altering the balance of top-down versus bottom-up information passing. The robust hypothesis-confirming nature of these findings imply the discovery of an important mechanistic principle underpinning psychedelic-induced altered states.

In recent years artificial neural networks achieved performance close to or better than humans in several domains: tasks that were previously human prerogatives, such as language processing, have witnessed remarkable improvements in state of the art models. One advantage of this technological boost is to facilitate comparison between different neural networks and human performance, in order to deepen our understanding of human cognition. Here, we investigate which neural network architecture (feedforward vs. recurrent) matches human behavior in artificial grammar learning, a crucial aspect of language acquisition. Prior experimental studies proved that artificial grammars can be learnt by human subjects after little exposure and often without explicit knowledge of the underlying rules. We tested four grammars with different complexity levels both in humans and in feedforward and recurrent networks. Our results show that both architectures can “learn” (via error back-propagation) the grammars after the same number of training sequences as humans do, but recurrent networks perform closer to humans than feedforward ones, irrespective of the grammar complexity level. Moreover, similar to visual processing, in which feedforward and recurrent architectures have been related to unconscious and conscious processes, the difference in performance between architectures over ten regular grammars shows that simpler and more explicit grammars are better learnt by recurrent architectures, supporting the hypothesis that explicit learning is best modeled by recurrent networks, whereas feedforward networks supposedly capture the dynamics involved in implicit learning.

Perception relies on hierarchical processes integrating sensory data into higher‐order models about the world. In the sensory domain, this hierarchy also involves horizontal pathways aiding interhemispheric interactions. For example, recent focus on the V5‐V5 network revealed its role in motion processing. However, despite converging evidence demonstrating the crucial role of oscillatory activity in inter‐areas communication, the specific rhythmic code governing V5‐V5 interaction is unclear. Through traveling wave analysis, this gap is filled by identifying the alpha/low‐beta frequency (9–16 Hz) as crucial for long‐range, interhemispheric communication between V5 regions. Notably, a pronounced imbalance is observed: stronger waves travelled in the left‐to‐right‐V5 direction. Crucially, this observation extends to the behavioural level, as Signal Detection Theory analysis uncovered a correlation between the highlighted imbalance and a perceptual bias favouring leftward motion reporting. Our findings underscore asymmetrical projections between V5 regions, highlighting the interplay between inter‐hemispheric traveling wave patterns in dictating motion perception. The study shows an asymmetric connectivity between visual motion areas, with alpha travelling waves favouring left‐to‐right (thick red arrow) over right‐to‐left (thin blue arrow) communication. This asymmetry predicts a bias toward reporting leftward (higher number of red dots) over rightward (lower number of blue dots) motion direction, regardless of the actual movement (equal number of left/right black arrows).

Predictive coding theory suggests that prior knowledge is crucial for optimizing human decision-making. However, how cortical dynamics implement this integration remains unclear. Here, we employ a traveling wave approach to investigate how alpha band oscillations integrate prior expectations during a perceptual decision-making task. Our findings demonstrate that expectation-based knowledge triggers the propagation of alpha band traveling waves from frontal to occipital areas, with this increase associated with enhanced modulation of brain regions involved in stimulus processing and directly linked to prior-driven bias at the behavioral level. Moreover, participants who rely more on prior expectations exhibit stronger top-down signaling (frontal-to-occipital), whereas those who focus on sensory input show a contrasting bottom-up pattern (occipital-to-frontal). These results highlight the role of alpha band traveling waves in predictive mechanisms, suggesting that rhythmic interactions across brain regions facilitate this process and contribute to inter-individual differences in its implementation. Alpha-band traveling waves carry predictive signals across the cortex. Backward waves encode prior expectations and tune sensory processing, revealing how individual brains integrate predictions during perceptual decisions.

Complex regional pain syndrome (CRPS) is a chronic pain condition associating sensory, motor, trophic and autonomic symptoms in one limb. Cognitive difficulties have also been reported, affecting the patients’ ability to mentally represent, perceive and use their affected limb. However, the nature of these deficits is still a matter of debate. Recent studies suggest that cognitive deficits are limited to body-related information and body perception, while not extending to external space. Here we challenge that statement, by using temporal order judgment (TOJ) tasks with tactile (i.e. body) or visual (i.e. extra-body) stimuli in patients with upper-limb CRPS. TOJ tasks allow characterizing cognitive biases to the advantage of one of the two sides of space. While the tactile TOJ tasks did not show any significant results, significant cognitive biases were observed in the visual TOJ tasks, affecting mostly the perception of visual stimuli occurring in the immediate vicinity of the affected limb. Our results clearly demonstrate the presence of visuospatial deficits in CRPS, corroborating the cortical contribution to the CRPS pathophysiology, and supporting the utility of developing rehabilitation techniques modifying visuospatial abilities to treat chronic pain.

Coordinating spatial perception between body space and its external surrounding space is essential to adapt behaviors to objects, especially when they are noxious. Such coherent multisensory representation of the body extended into external space is conceptualized by the notion of peripersonal reference frame, mapping the portion of space in which somatic and extra-somatic inputs interact closely. Studies on crossmodal interactions between nociception and vision have been scarce. Here we investigated how the perception of visual stimuli, especially those surrounding the body, can be impacted by a nociceptive and potentially harmful stimulus inflicted on a particular body part. In two temporal order judgment tasks, participants judged which of two lateralized visual stimuli, presented either near or far from the body, had been presented first. Visual stimuli were preceded by nociceptive stimuli, either applied unilaterally (on one single hand) or bilaterally (on both hands simultaneously). In Experiment 1 participants' hands were always placed next to the visual stimuli presented near the trunk, while in Experiment 2 they could also be placed next to the visual stimuli presented far from the trunk. In Experiment 1, the presence of unilateral nociceptive stimuli prioritized the perception of visual stimuli presented in the same side of space as the stimulated hand, with a significantly larger effect when visual stimuli were presented near the body than when presented farther away. Experiment 2 showed that these visuospatial biases were related to the spatial congruency between the hand on which nociceptive stimuli were applied and the visual stimuli, independently of the relative distance of both the stimulated hand and the visual stimuli from the trunk. Indeed, nociceptive stimuli mostly impacted the perception of the closest visual stimuli. It is hypothesized that these crossmodal interactions may rely on representations of the space directly surrounding specific body parts.

Beta-band oscillations have been suggested to promote the maintenance of the current motor (or cognitive) set, thus signaling the ‘status quo’ of the system. While this hypothesis has been reliably demonstrated in many studies, it fails to explain changes in beta-band activity due to the accumulation of physical fatigue. In the current study, we aimed to reconcile the functional role of beta oscillations during physical fatigue within the status quo theory. Using an innovative electroencephalography design, we identified two distinct beta-band power dynamics in the motor areas as fatigue rises: (i) an enhancement at rest, supposedly promoting the resting state, and (ii) a decrease during contraction, thought to reflect the increase in motor cortex activation necessary to cope with muscular fatigue. We then conducted effective connectivity analyses, which revealed that the modulations during contractions were driven by frontal areas. Finally, we implemented a biologically plausible model to replicate and characterize our results mechanistically. Together, our findings anchor the physical fatigue paradigm within the status quo theory, thus shedding light on the functional role of beta oscillations in physical fatigue. We further discuss a unified interpretation that might explain the conflicting evidence previously encountered in the physical fatigue literature. This work shows for the first time two distinct beta-band dynamics in the motor cortex during a task eliciting physical fatigue. It further explores the prefrontal influence on motor cortex activity, supported by connectivity analyses and modelling.