Anticipatory postural adjustments (APA) counteract predicted postural disturbances, emerge early in life, and undergo prolonged maturation, yet their neural mechanisms remain poorly understood. Using magnetoencephalography during a naturalistic bimanual load-lifting task, we studied anticipatory postural control in children aged 7-12. Lifting a load from the contralateral forearm induces upward elbow rotation, anticipatorily counteracted by inhibition of Biceps brachii. Despite greater variability and less efficient stabilization in children, inhibition relied on the same mechanism described in adults: transient beta bursts (19-24Hz), linked to reduced supplementary motor area (SMA) excitability, indexed by high-gamma suppression, and preceded by prefrontal and premotor influences. However, children showed lower burst frequency and additionally recruited higher-frequency bursts (24-29Hz) associated with delayed, alpha-mediated SMA suppression that attenuated post-inhibition instability. These findings demonstrate that middle-childhood children already engage adult-like APA mechanisms, but also compensate for predicted postural imprecision, offering insight into APA maturation and its alteration in neurodevelopmental conditions.

The alpha rhythm, first identified by Hans Berger 100 years ago, is the dominant noninvasive electrophysiological signature of the healthy human brain in the awake state. For decades, it was believed that the alpha rhythm reflected rest or idling; however, this perspective changed in the 2000s when researchers found that alpha oscillations increase with cognitive demands. This discovery led to a paradigm shift, demonstrating that alpha oscillations reflect the functional inhibition of brain regions that are not needed for a specific task, thereby directing information to task-specific areas. We have reviewed the physiological mechanisms involved in generating alpha oscillations, which have informed computational models explaining how these oscillations emerge within physiologically realistic networks. At the behavioral level, alpha oscillations are strongly modulated across nearly all cognitive paradigms tested in humans, reflecting the allocation of computational resources within the active brain network. Research in individuals with attention-related issues has highlighted their impaired ability to modulate alpha oscillations, which is associated with performance deficits. Therefore, further exploration of alpha oscillations has the potential to uncover causal mechanisms underlying attention problems, such as those related to attention deficit hyperactivity disorder (ADHD) and aging. Finally, advancements in technology are opening new avenues for characterizing alpha oscillations in ecologically valid settings and across the lifespan. This progress sets the stage for exploring the role of alpha oscillations in cognitive development and their functioning in natural environments.

In motor networks, inhibition has been associated with oscillatory activity in the mu (8 to 12 Hz) or beta (13 to 30 Hz) bands, yet how these rhythms ultimately influence muscle activity remains unclear. The bimanual load-lifting task (BLLT) elicits anticipatory inhibition of the elbow flexors in the load-supporting arm during voluntary load-lifting, providing a suitable model to study oscillatory mechanisms of muscle inhibition. We recorded magnetoencephalography in adult participants performing the BLLT. Optimal postural stabilization occurred when biceps brachii inhibition preceded unloading by ~10 to 40 ms. Stronger muscle inhibition within this time window was associated with reduced high-gamma (90 to 130 Hz) power, potentially reflecting reduced excitability, and increased high-beta power in the contralateral supplementary motor area (SMA), with high-gamma power reduction partially mediating the effect of high-beta power on anticipatory inhibition. Furthermore, trials containing beta bursts (22 to 28 Hz) exhibited both optimally timed anticipatory inhibition and concurrent reduction in high-gamma power. These findings suggest that optimal anticipatory inhibition in the load-supporting elbow flexor is linked to reduced SMA excitability mediated by inhibitory beta bursts. More broadly, they provide new evidence linking beta bursts to the inhibitory control of muscle function and contribute to a better understanding of motor anticipation.

The role of cortical oscillations in brain function has been extensively debated, resulting in a variety of theoretical frameworks. Using interleaved simultaneous electroencephalography–functional magnetic resonance imaging, we examined the layer-specific relationship between oscillatory activity and visual processing. We could demonstrate that γ band activity positively correlates with feature-specific signals in superficial layers, but we were able to report a deep layer contribution as well. In addition, we could demonstrate that α band power not only correlates negatively with the feature-unspecific BOLD signal but is related to feature-specific BOLD as well. Lower frequency α was predominantly related to feature-unspecific superficial layer BOLD, while upper frequency α was found to be related to feature-specific BOLD in superficial and deep layers. We conclude that the role of α band oscillations extends beyond widespread inhibition and might be involved in active stimulus processing on the level of visual features.

In motor networks, motor inhibition can be driven by sensorimotor mu rhythm (8-12Hz) or beta bursts (13-30Hz). In this study, we aimed to investigate whether mu or beta activity supports efficient anticipatory inhibition, as reflected by a decrease in electromyographic (EMG) activity. To test this, we recorded magnetoencephalography (MEG) in 16 adults performing a Bimanual Load Lifting Task (BLLT), where participants lifted a load with one hand supported by the other. In anticipation of unloading, elbow flexors in the supporting arm are inhibited to prevent elbow deflection. We observed that optimal postural stabilization occurs when flexor inhibition happens approximately 30 ms before unloading begins. Stronger EMG inhibition in this time interval correlated negatively with high-gamma power (90-130Hz), reflecting reduced neural excitability, and positively with high-beta power in the medial supplementary motor area (SMA). In contrast, no significant correlation was observed in the mu-range (8-12 Hz). Meanwhile, high-beta and high-gamma power were negatively correlated. Mediation analysis confirmed that gamma power significantly mediates the relationship between beta power and EMG inhibition. Beta burst probability and directed connectivity analysis using the Phase Slope Index indicated that high-beta bursts are transmitted from the middle prefrontal cortex (mPFC) and elbow-related primary motor cortex (M1) to the SMA. Our findings suggest that, in the voluntary unloading task, anticipatory muscle inhibition at the optimal time is driven by a reduction in excitability within the SMA, likely facilitated by high-beta bursts originating from the mPFC-M1-SMA network. ### Competing Interest Statement The authors have declared no competing interest.

Attention is a fundamental mechanism enabling the brain to overcome its limited capacity for parallel processing. In non-human primates, invasive electrophysiology has shown that attentional selection operates rhythmically, primarily within the alpha (∼8–12 Hz) and theta (∼4–5 Hz) bands. Whether such finely resolved control signals can be captured non-invasively in humans, and how they adapt to changing task demands, remains unclear. Using high-precision magnetoencephalography (MEG) combined with machine learning, we decoded the spatial locus of covert attention in humans performing three variants of a spatial cueing task that manipulated cue validity as well invalid trial switching rules. Spatial attention could be decoded from whole-brain MEG activity at both static and time-resolved scales, with accuracies significantly above chance (N = 30). Decoding performance decreased as cue validity was reduced, indicating that task structure shapes attentional engagement. Analysis of decoding trajectories revealed rhythmic fluctuations at ∼8–12 Hz across all tasks, demonstrating alpha-band sampling of attention. Pre-target attention became increasingly focused on the cued side, especially in the 100% Valid condition, consistent with proactive orienting. Furthermore, individual and task-specific differences in decoding strength correlated with task-variations in behavioral performance, linking the accuracy of neural attention codes to both discrimination accuracy and reaction time. These findings demonstrate that MEG can non-invasively capture dynamic, task-dependent fluctuations in spatial attention that parallel those observed in non-human primates. They reveal that attentional demands reshape the neural code for attention, modulate rhythmic sampling, and influence behavioral efficiency. This work bridges invasive primate and non-invasive human research and establishes MEG-based decoding of attention as a promising tool for mechanistic and clinical applications, including neurofeedback and attention-related interventions. ### Competing Interest Statement The authors have declared no competing interest. Agence Nationale de la Recherche, ANR-11-LABX-0042, ANR-11-IDEX-0007 European Research Council, #716862

Abstract Neural dynamics at the laminar level are critical for cortical computation. However, in humans, non-invasive methods to probe such dynamics have been limited to coarse distinctions between deep and superficial layers. Here, we present a multilayer magnetoencephalography source reconstruction framework and evaluate the conditions under which depth-resolved laminar inference may be feasible. Using simulations, we systematically assess the limits of magnetoencephalography depth resolution, showing that laminar discrimination depends on sufficiently high signal-to-noise ratio, precise co-registration, and accurate specification of cortical column orientation. We demonstrate that regional variations in cortical anatomy influence reconstruction fidelity, with lead-field separability emerging as a key determinant. We then apply this framework to empirical data from three independent datasets and find laminar activation patterns that align with canonical feedforward and feedback motifs in visual and sensorimotor circuits, supporting the plausibility of laminar inference under favorable conditions and offering opportunities to bridge invasive electrophysiology and human neuroimaging.

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

The complexity of natural environments requires highly flexible mechanisms for adaptive processing. Furthermore, the processing of single and multiple stimuli is strongly context and goal dependent. The large repertoire of frequencies and waveforms of what is commonly referred to as neuronal oscillations could be an ideal candidate for implementing such flexibility in neural systems. Here we present a framework in which highly dynamic multiplexing and phase coding schemes underlie and structure the attention-guided processing of complex visual scenes. 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 sustained oscillations. We propose that different elements of a single object should be processed within a single cycle (or burst) of alpha band activity (7-14Hz). This allows for the formation of coherent object representations while simultaneously separating multiple objects across multiple alpha cycles. Each element (e.g. the eyes or ears of the object cat) would still be processed separately in time - expressed as different gamma band bursts (> 30Hz) - 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. The specific frequency and wave-shape of the respective fluctuations involved would depend on the nature of the object that is processed and on cognitive demands. Additionally, we suggest that the processing of multiple objects would further be organized along the phase of flexible slower fluctuations (e.g. delta or theta). Alternatively, saccades could drive the reset of this slow fluctuations. Complex scene processing and exploration, involving covert attention and eye movements, would therefore be associated with multiple frequency changes, both in the alpha and lower frequency range. This framework therefore embraces the idea of a hierarchical organization of visual scene processing, independent of the temporal dynamics of the environment.

The particular role of cortical oscillations has been a long-debated topic that resulted in a variety of theoretical frameworks. Oscillatory activity in the [alpha] band has been associated with sensory processing, attention as well as other cognitive functions, while [gamma] band oscillations is thought to be related to stimulus feature processing. Current theoretical frameworks rely on the separation of the cortical architecture into layers. Recently, methodological advancements have allowed to test layer specific frameworks on the role of oscillations in cortical computations in healthy human participants. Using EEG fMRI, we have investigated for the first time both, stimulus feature specificity (line orientation) and the relationship between the laminar BOLD activity and [alpha] and γ band oscillations. We find γ oscillations to be positively correlated with feature specific signals in superficial layers as predicted by the literature, but we found a deep layer contribution as well. Furthermore we found a layer (and frequency) dissociation within the α band for general, feature unspecific, processes and a feature related process. The power of the α-band correlated negatively with feature unspecific neural activity in all cortical layers. We further found that high frequency α oscillations were specifically related to stimulus feature specific BOLD signal in deep and superficial layers. More interestingly, we also observed a general modulation effect for negative BOLD signal deflections in line with the inhibitory role of α during visual attention in superficial layers. Those findings support the association of γ band oscillations with visual feature processing and further point towards the involvement of multiple α oscillations in more general and feature related processes. ### Competing Interest Statement The authors have declared no competing interest.

Temporal predictions can be formed and impact perception when sensory timing is fully predictable: for instance, the discrimination of a target sound is enhanced if it is presented on the beat of an isochronous rhythm. However, natural sensory stimuli, like speech or music, are not entirely predictable, but still possess statistical temporal regularities. We investigated whether temporal expectations can be formed in non-fully predictable contexts, and how the temporal variability of sensory contexts affects auditory perception. Specifically, we asked how "rhythmic" an auditory stimulation needs to be in order to observe temporal predictions effects on auditory discrimination performances. In this behavioral auditory oddball experiment, participants listened to auditory sound sequences where the temporal interval between each sound was drawn from gaussian distributions with distinct standard deviations. Participants were asked to discriminate sounds with a deviant pitch in the sequences. Auditory discrimination performances, as measured with deviant sound discrimination accuracy and response times, progressively declined as the temporal variability of the sound sequence increased. Moreover, both global and local temporal statistics impacted auditory perception, suggesting that temporal statistics are promptly integrated to optimize perception. Altogether, these results suggests that temporal predictions can be set up quickly based on the temporal statistics of past sensory events and are robust to a certain amount of temporal variability. Therefore, temporal predictions can be built on sensory stimulations that are not purely periodic nor temporally deterministic.

The role of alpha oscillations (8-13Hz) in suppressing distractors has been extensively debated. Some studies suggest that alpha oscillations support distractor suppression by increasing in regions processing anticipated distractors. However, other studies did not reproduce this effect. We identify the methodological differences in experimental designs that may explain these discrepancies. Another debate centers on the mechanistic role of alpha oscillations. While we and others have proposed alpha oscillations implementing a gain reduction in early visual regions when e.g. target load or distractor interference are high, we suggest that parietal alpha oscillations support gating or stabilization of the attentional focus and alpha in ventral attention network implement resistance to distraction. We will outline future studies needed to identify the task contexts required to uncover the precise mechanistic role of alpha oscillations.

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

MagnetoEncephaloGraphy (MEG) provides a measure of electrical activity in the brain at a millisecond time scale. From these signals, one can non-invasively derive the dynamics of brain activity. Conventional MEG systems (SQUID-MEG) use very low temperatures to achieve the necessary sensitivity. This leads to severe experimental and economical limitations. A new generation of MEG sensors is emerging: the optically pumped magnetometers (OPM). In OPM, an atomic gas enclosed in a glass cell is traversed by a laser beam whose modulation depends on the local magnetic field. MAG4Health is developing OPMs using Helium gas (4He-OPM). They operate at room temperature with a large dynamic range and a large frequency bandwidth and output natively a 3D vectorial measure of the magnetic field. In this study, five 4He-OPMs were compared to a classical SQUID-MEG system in a group of 18 volunteers to evaluate their experimental performances. Considering that the 4He-OPMs operate at real room temperature and can be placed directly on the head, our assumption was that 4He-OPMs would provide a reliable recording of physiological magnetic brain activity. Indeed, the results showed that the 4He-OPMs showed very similar results to the classical SQUID-MEG system by taking advantage of a shorter distance to the brain, despite having a lower sensitivity.

ABSTRACTReorienting attention to unexpected events is essential in daily life. fMRI studies have revealed the involvement of the ventral attention network (VAN), including the temporo-parietal junction (TPJ), in such process. In this MEG study with 34 participants (17 women) we used a bimodal (visual/auditory) attention task to determine the neuronal dynamics associated with suppression of the activity of the VAN during top-down attention and its recruitment when information from the unattended sensory modality is involuntarily integrated. We observed an anticipatory power increase of alpha/beta (12-20 Hz) oscillations in the VAN following a cue indicating the modality to attend. Stronger VAN power increases predicted better task performance, suggesting that the VAN suppression prevents shifting attention to distractors. Moreover, the TPJ was synchronized with the frontal eye field in that frequency band, suggesting that the dorsal attention network (DAN) might participate in such suppression. Furthermore, we found a 12-20 Hz power decrease, in both the VAN and DAN, when information of both sensory modalities was congruent, suggesting an involvement of these networks for attention capture. Our results show that effective multimodal attentional reorientation includes the modulation of the VAN and DAN through upper-alpha/beta oscillations. Altogether these results indicate that the suppressing role of alpha/beta oscillations might operate beyond sensory regions.SIGNIFICANCE STATEMENTReorienting attention to unexpected events from multiple sensory sources is essential in daily life. We explored the dynamics of the ventral attention network (VAN), a set of brain regions related to attentional reorienting, when relevant information was anticipated (i.e. during top-down attention) and when unexpected congruent information from another sensory modality was presented (involuntary attentional capture). We report that activity in the alpha/beta range (12-20 Hz) within the VAN indexed both top-down and attentional capture processes. Also, the VAN was synchronized with the dorsal attention network in this frequency band, suggesting an integrated role of both networks for attentional regulation. Our results shed light on the neurophysiological mechanisms that the brain carry out for reorienting attention to relevant environmental stimuli.

Determining the anatomical source of brain activity non-invasively measured from EEG or MEG sensors is challenging. In order to simplify the source localization problem, many techniques introduce the assumption that current sources lie on the cortical surface. Another common assumption is that this current flow is orthogonal to the cortical surface, thereby approximating the orientation of cortical columns. However, it is not clear which cortical surface to use to define the current source locations, and normal vectors computed from a single cortical surface may not be the best approximation to the orientation of cortical columns. We compared three different surface location priors and five different approaches for estimating dipole vector orientation, both in simulations and visual and motor evoked MEG responses. We show that models with source locations on the white matter surface and using methods based on establishing correspondences between white matter and pial cortical surfaces dramatically outperform models with source locations on the pial or combined pial/white surfaces and which use methods based on the geometry of a single cortical surface in fitting evoked visual and motor responses. These methods can be easily implemented and adopted in most M/EEG analysis pipelines, with the potential to significantly improve source localization of evoked responses.

Alpha oscillations (8–14 Hz) are proposed to represent an active mechanism of functional inhibition of neuronal processing. Specifically, alpha oscillations are associated with pulses of inhibition repeating every ∼100 msec. Whether alpha phase, similar to alpha power, is under top–down control remains unclear. Moreover, the sources of such putative top–down phase control are unknown. We designed a cross-modal (visual/auditory) attention study in which we used magnetoencephalography to record the brain activity from 34 healthy participants. In each trial, a somatosensory cue indicated whether to attend to either the visual or auditory domain. The timing of the stimulus onset was predictable across trials. We found that, when visual information was attended, anticipatory alpha power was reduced in visual areas, whereas the phase adjusted just before the stimulus onset. Performance in each modality was predicted by the phase of the alpha oscillations previous to stimulus onset. Alpha oscillations in the left pFC appeared to lead the adjustment of alpha phase in visual areas. Finally, alpha phase modulated stimulus-induced gamma activity. Our results confirm that alpha phase can be top–down adjusted in anticipation of predictable stimuli and improve performance. Phase adjustment of the alpha rhythm might serve as a neurophysiological resource for optimizing visual processing when temporal predictions are possible and there is considerable competition between target and distracting stimuli.

Unraveling how brain regions communicate is crucial for understanding how the brain processes external and internal information. Neuronal oscillations within and across brain regions have been proposed to play a crucial role in this process. Two main hypotheses have been suggested for routing of information based on oscillations, namely communication through coherence and gating by inhibition. Here, we propose a framework unifying these two hypotheses that is based on recent empirical findings. We discuss a theory in which communication between two regions is established by phase synchronization of oscillations at lower frequencies (<25 Hz), which serve as temporal reference frame for information carried by high-frequency activity (>40 Hz). Our framework, consistent with numerous recent empirical findings, posits that cross-frequency interactions are essential for understanding how large-scale cognitive and perceptual networks operate.