Biology subjects

Bonaiuto, J. J.

Publications and source records attributed to Bonaiuto, J. J..

18 recordsLinked to original sources

bioRxiv2026

Investigating sensorimotor beta burst dynamics as a robust biomarker for graded force modulation in humans

The most prominent signature associated with motor execution and motor imagery is the event-related desynchronisation and synchronisation (ERD/S) in the mu and beta bands (8-30 Hz). In the context of brain-computer interfaces (BCI), this ERD/S signature is helpful for binary decisions, such as left vs. right imagery, but it is not a robust biomarker for continuous prediction, such as precisely decoding different levels of force application. This is essential for developing better BCI applications with precise dynamic force outputs. Recent studies have revealed that sensorimotor beta bursts have a stronger relationship with motor control, even at a single-trial level, than mu and beta frequency band power. We, therefore, investigated whether the transient nature of beta bursts provide an alternative, but robust biomarker for BCI force decoding. Here, we designed an experiment where human participants (N = 16) performed both motor execution (ME) at four force levels (10%, 25%, 50%, and 75% of maximum voluntary contraction) and followed by imagination of the same, i.e. a motor imagery (MI) task, as their electroencephalogram (EEG) was recorded. We observed a clear and classical ERD pattern in the motor cortex region during the ME task, whereas it was less pronounced during the MI task. To explore the robust biomarkers for explanation of the force modulation we moved beyond classic trial average ERD/S and analysed the role of beta burst. After extracting sensorimotor beta bursts, we observed differences in spectral burst features between motor execution and imagery including burst amplitude, spectral width, and temporal width. Moreover, different force levels were correlated with changes in the burst amplitude and burst spectral width, specifically during motor execution. Interestingly, we found that different beta burst waveforms are associated with the different force levels and conditions. This suggests that the bursts-level features could be driven by changes in the underlying beta burst waveforms. Overall, our study shows that sensorimotor beta burst can be an important piece of the puzzle to implementing precise force control in brain-computer interface based prosthetics.

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bioRxiv2026

Distinct beta burst motifs exhibit opposing error relationships during motor adaptation

Beta-band activity (13-30 Hz) is a hallmark of human movement, yet a unifying account of its functional role remains unresolved. Although typically described as a sustained oscillation, beta activity is increasingly recognised to consist of transient bursts. More recently, beta bursts have been shown to exhibit heterogeneous waveforms. Here, we ask whether variability in burst shape corresponds to separable computational roles during motor adaptation. Using high-density MEG, we recorded neural activity while participants performed a visuomotor rotation task under either implicit (sensorimotor adaptation) or explicit (strategic re-aiming) learning conditions. Conventional metrics, beta power and burst rate, showed context-dependent modulation during preparation but provided limited insight into trial-by-trial behaviour. In contrast, sorting bursts according to their waveforms revealed a repertoire of burst types with dissociable temporal dynamics and context-dependent modulation. Crucially, during post-movement evaluation, distinct burst subtypes showed opposing and temporally specific relationships with behavioural error: one subtype decreased with increasing error, whereas others increased. Together, these findings indicate that beta activity comprises separable transient events with specific computational roles, and that accounting for waveform diversity is essential for understanding how cortical beta supports adaptive behaviour.

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bioRxiv2025

Sensorimotor beta burst dynamics and interhemispheric connectivity in children with developmental coordination disorder

Activity in the beta frequency band appears to play a critical role in sensorimotor processing, with transient bursts exhibiting distinct waveform motifs that may reflect different neural computations. However, how these dynamics relate to motor development and neurodevelopmental conditions remains unclear. Here, we used EEG to investigate sensorimotor beta bursts in children with and without developmental coordination disorder (DCD). We characterized beta bursts during action execution and observation of gross and fine motor actions. Conventional spectral features and overall burst rates did not differ between groups. In contrast, children with DCD showed altered rate modulation of specific burst waveform motifs, including atypical hemispheric lateralization and disrupted interhemispheric connectivity. Critically, motif-specific burst rates were systematically related to motor performance across individuals, with similar relations observed across groups. These findings indicate that waveform-specific beta burst dynamics track graded variation in motor performance, rather than mapping selectively onto diagnostic status. Beta burst variability may therefore reflect the functional expression of motor performance, rather than a disorder-specific neural mechanism underlying DCD. This work highlights the importance of beta burst waveform diversity for understanding variability in sensorimotor function across development.

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bioRxiv2025

Beta burst waveform extraction using novel 4He-OPMs

Studying the electrophysiology of motor preparation and execution is challenging due to the restrictions often placed on the experimental paradigm by the imaging modality. MEG is well suited to track temporal brain dynamics while offering good spatial resolution, but requires an absence of head motion due to the fixed helmet and associated cryogenic cooling system. Here we used novel, room temperature, wearable optically pumped magnetometers using Helium to measure the MEG in a motor preparation and execution task and compare this with classic SQUID-MEG. Beta band activity (13-30Hz) is widely associated with motor activity, and it has become widely accepted that beta activity occurs in bursts rather than sustained activity. Here we used a novel beta burst extraction pipeline to look at not only the occurrence of the beta bursts across the timecourse of motor preparation and execution, but at the specific waveforms of the beta burst that evolve over time. Results show that specific beta burst waveforms show strong task relevant modulations of burst rate. Beta burst waveforms extracted using Helium-OPMs were very similar to those extracted using SQUID-MEG, with comparable modulations of burst rate over time for specific waveforms. This shows a promising step to obtaining high quality electrophysiological data in less restricted paradigms.

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bioRxiv2025

Beyond deep versus superficial: true laminar inference with MEG

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.

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bioRxiv2024

Multi-scale parameterization of neural rhythmicity with lagged Hilbert autocoherence

Analysis of neural activity in different frequency bands is ubiquitous in systems and cognitive neuroscience. Recent analytical breakthroughs and theoretical developments rely on phase maintenance of oscillatory signals without considering whether or not this assumption is met. Lagged (auto)coherence, the coherence between a signal and itself at increasing temporal delays, has been proposed as a way to quantify the rhythmicity, or periodicity, of a signal. However, current Fourier-based lagged autocoherence algorithms suffer from poor spectral accuracy and resolution, aliasing effects that become more pronounced at higher frequencies, and conflation with amplitude covariation, especially in frequency ranges in which the signal-to-noise ratio is low. We introduce a continuous estimator, lagged Hilbert autocoherence (LHaC), which addresses these limitations by using multiplication in the frequency domain for precise bandpass filtering, computing instantaneous analytic signals via the Hilbert transform, and thresholding using the amplitude covariation of phase-shuffled surrogate data. While LHaC and lagged Fourier autocoherence (LFaC) estimate distinct theoretical quantities, we compare their empirical behavior in simulations with controlled rhythmic structure. These analyses show that LHaC provides more spectrally resolved estimates of rhythmicity and is more sensitive to the duration of transient, short-lived oscillatory events. We further demonstrate the utility of LHaC for identifying frequency-specific differences in rhythmicity between conditions and tracking learning-related changes in neural oscillations. Lagged Hilbert autocoherence thus offers a refined and practically useful approach to characterizing neurophysiological rhythmicity.

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bioRxiv2024

Surfing beta burst waveforms to improve motor imagery-based BCI

Our understanding of motor-related, macroscale brain processes has been significantly shaped by the description of the event-related desynchronization (ERD) and synchronization (ERS) phenomena in the mu and beta frequency bands prior to, during and following movement. The demonstration of reproducible, spatially-and band-limited signal power changes has, consequently, attracted the interest of non invasive brain-computer interface (BCI) research for a long time. BCIs often rely on motor imagery (MI) experimental paradigms that are expected to generate brain signal modulations analogous to movement-related ERD and ERS. However, a number of recent neuroscience studies has questioned the nature of these phenomena. Beta band activity has been shown to occur, on a single-trial level, in short, transient and heterogeneous events termed bursts rather than sustained oscillations. In a previous study, we established that an analysis of hand MI binary classification tasks based on beta bursts can be superior to beta power in terms of classification score. In this article we elaborate on this idea, proposing a signal processing algorithm that is comparable to-and compatible with state-of-the-art techniques. Our pipeline filters brain recordings by convolving them with kernels extracted from beta bursts and then applies spatial filtering before classification. This data-driven filtering allowed for a simple and efficient analysis of signals from multiple sensors thus being suitable for online applications. By adopting a time-resolved decoding approach we explored MI dynamics and showed the specificity of the new classification features. In accordance with previous results, beta bursts improved classification performance compared to beta band power, while often increasing information transfer rate compared to state-of-the-art approaches. Significance statementPatterns of waveform-specific burst rate comprise an alternative, neurophysiology-informed way of analyzing beta band activity during motor imagery (MI) tasks. By testing this method on multiple electroencephalography datasets and comparing its corresponding classification scores against those of conventional power-based features, this work demonstrates that brain-computer interface applications could benefit from utilizing beta burst activity. This activity gives access to a reliable decoding performance often requiring short recordings. As such, this study shows that waveform-specific beta burst rates encode information related to imagined (and presumably real) movements and serves as the first step for a real-time implementation of the proposed methodology.

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bioRxiv2023

Beta bursts question the ruling power for brain-computer interfaces

Current efforts to build reliable brain-computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience. In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in "left vs. right hand" motor imagery tasks. Current decoding approaches for such tasks take advantage of the fact that motor imagery generates time-locked changes in induced power in the sensorimotor cortex, and rely on band-pass filtered power changes or covariance matrices which also describe co-varying power changes in signals recorded from different channels. Although little is known about the dynamics of beta burst activity during motor imagery, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements. We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these bio-markers.

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bioRxiv2023

Bursting with potential: How sensorimotor beta bursts develop from infancy to adulthood

Beta activity is thought to play a critical role in sensorimotor processes. However, little is known about how activity in this frequency band develops. Here, we investigated the developmental trajectory of sensorimotor beta activity from infancy to adulthood. We recorded electroencephalography (EEG) from adults, 12-month-olds, and 9-month-olds while they observed and executed grasping movements. We analysed beta burst activity using a novel method that combines time-frequency decomposition and principal component analysis (PCA). We then examined the changes in burst rate and waveform motifs along the selected principal components. Our results reveal systematic changes in beta activity during action execution across development. We found a decrease in beta burst rate during movement execution in all age groups, with the greatest decrease observed in adults. Additionally, we identified four principal components that defined waveform motifs that systematically changed throughout the trial. We found that bursts with waveform shapes closer to the median waveform were not rate-modulated, whereas those with waveform shapes further from the median were differentially rate-modulated. Interestingly, the decrease in the rate of certain burst motifs occurred earlier during movement and was more lateralized in adults than in infants, suggesting that the rate modulation of specific types of beta bursts becomes increasingly refined with age.

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bioRxiv2022

Diverse beta burst waveform motifs characterize movement-related cortical dynamics

Classical analyses of induced, frequency-specific neural activity typically average bandlimited power over trials. More recently, it has become widely appreciated that in individual trials, beta band activity occurs as transient bursts rather than amplitude-modulated oscillations. Most studies of beta bursts treat them as unitary, and having a stereotyped waveform. However, we show there is a wide diversity of burst shapes. Using a biophysical model of burst generation, we demonstrate that waveform variability is predicted by variability in the synaptic drives that generate beta bursts. We then use a novel, adaptive burst detection algorithm to identify bursts from human MEG sensor data recorded during a joystick-based reaching task, and apply principal component analysis to burst waveforms to define a set of dimensions, or motifs, that best explain waveform variance. Finally, we show that bursts with a particular range of waveform motifs, ones not fully accounted for by the biophysical model, differentially contribute to movement-related beta dynamics. Sensorimotor beta bursts are therefore not homogeneous events and likely reflect distinct computational processes.

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bioRxiv2022

Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex

Neural populations, rather than single neurons, may be the fundamental unit of cortical computation. Analyzing chronically recorded neural population activity is challenging not only because of the high dimensionality of activity in many neurons, but also because of changes in the recorded signal that may or may not be due to neural plasticity. Hidden Markov models (HMMs) are a promising technique for analyzing such data in terms of discrete, latent states, but previous approaches have either not considered the statistical properties of neural spiking data, have not been adaptable to longitudinal data, or have not modeled condition specific differences. We present a multilevel Bayesian HMM which addresses these shortcomings by incorporating multivariate Poisson log-normal emission probability distributions, multilevel parameter estimation, and trial-specific condition covariates. We applied this framework to multi-unit neural spiking data recorded using chronically implanted multi-electrode arrays from macaque primary motor cortex during a cued reaching, grasping, and placing task. We show that the model identifies latent neural population states which are tightly linked to behavioral events, despite the model being trained without any information about event timing. We show that these events represent specific spatiotemporal patterns of neural population activity and that their relationship to behavior is consistent over days of recording. The utility and stability of this approach is demonstrated using a previously learned task, but this multilevel Bayesian HMM framework would be especially suited for future studies of long-term plasticity in neural populations.

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bioRxiv2022

Spatiotemporal organization of human sensorimotor beta burst activity

Beta oscillations in human sensorimotor cortex are hallmark signatures of healthy and pathological movement. In single trials, beta oscillations include bursts of intermittent, transient periods of high-power activity. These burst events have been linked to a range of sensory and motor processes, but their precise spatial, spectral, and temporal structure remains unclear. Specifically, a role for beta burst activity in information coding and communication suggests spatiotemporal patterns, or travelling wave activity, along specific anatomical gradients. We here show in human magnetoencephalography recordings that burst activity in sensorimotor cortex occurs in planar spatiotemporal wave-like patterns that dominate along two axes either parallel or perpendicular to the central sulcus. Moreover, we find that the two propagation directions are characterised by distinct anatomical and physiological features. Finally, our results suggest that sensorimotor beta bursts occurring before and after a movement share the same generator but can be distinguished by their anatomical, spectral and spatiotemporal characteristics, indicating distinct functional roles.

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bioRxiv2022

High precision magnetoencephalography reveals increased right-inferior frontal gyrus beta power during response conflict

Flexibility of behavior and the ability to rapidly switch actions is critical for adaptive living in humans. It is well established that the right-inferior frontal gyrus (R-IFG) is recruited during outright action-stopping, relating to increased beta (12-30 Hz) power. Additionally, pre-supplementary motor area (pre-SMA) is plausibly recruited during response conflict/switching, relating to increased theta (4-8 Hz) power. It has been posited that inhibiting incorrect response tendencies is central to motor flexibility. However, it is not known if the commonly reported R-IFG beta signature of response inhibition in action-stopping is also recruited during response conflict, which would suggest overlapping networks for stopping and switching. In the current study, we analyzed high precision magnetoencephalography (hpMEG) data recorded with very high trial numbers (total n > 10,000) from 8 subjects during different levels of response conflict. We hypothesized that a R-IFG-triggered network for response inhibition is domain general and also involved in mediating response conflict. We therefore tested whether R-IFG showed increased beta power dependent on the level of response conflict. We also hypothesized that pre-SMA is an important node in response conflict processing, and tested whether pre-SMA theta power increased for response conflict trials. Using event-related spectral perturbations and linear mixed modeling, we found that both R-IFG beta and pre-SMA theta increased for response conflict trials, with the R-IFG beta increase specific to trials with strong response conflict. This result supports a more generalized role for R-IFG beta in response inhibition, beyond simple stopping behavior towards response switching. Significance StatementResponse inhibition is a core component of cognitive control. Neural mechanisms of response inhibition are typically studied using stopping paradigms. However, there is an unresolved debate regarding whether the response inhibition network is specific to stopping or generalizes to switching between tasks and overcoming conflict between competing response tendencies. Increased beta (12-30 Hz) in R-IFG has historically been interpreted as a marker of successful response inhibition in the stop-signal task. Here, we investigated the presence of this electrophysiological marker of response inhibition specifically during response conflict (switching). We found R-IFG beta power increased for trials with strong response conflict, and not for weak or no response conflict, thereby supporting a generalized role for R-IFG beta in response inhibition and switching.

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bioRxiv2021

Laminar dynamics of beta bursts in human motor cortex

Modulation of motor cortical activity in the beta frequency range is one of the strongest and most studied movement-related neural signals. At the single trial level, beta band activity is often characterized by transient bursting events rather than slowly modulating oscillations, suggesting a more rapid, information-encoding functional role than previously believed. Insight into how beta bursts are generated in sensorimotor circuits can provide important constraints to theories about their functional role for movement control. To this end, we leverage and extend recent developments in high precision MEG for temporally resolved laminar analysis of burst activity, combined with a neocortical circuit model that simulates the biophysical generators of the electrical currents which drive beta bursts. This approach pinpoints the generation of beta bursts in human motor cortex to distinct excitatory synaptic inputs to deep and superficial cortical layers, which drive current flow in opposite directions. These laminar dynamics of beta bursts in motor cortex align with prior invasive animal recordings within the somatosensory cortex, and suggest a conserved mechanism for somatosensory and motor cortical beta bursts. More generally, we demonstrate the ability for uncovering the laminar dynamics of event-related neural signals in human non-invasive recordings.

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bioRxiv2020

Dynamic causal modeling of layered magnetoencephalographic event-related responses

The layered architecture of cortex is thought to play a fundamental role in shaping cortical computations. However, direct electrophysiological measurements of layered activity are not possible non-invasively in humans. Recent advances have shown that a distinction of two layers can be achieved using magnetoencephalography in combination with head casts and advanced spatial modeling. In this technical note, we present a dynamic causal model of a single cortical microcircuit that models event related potentials. The model captures the average dynamics of a detailed two layered circuit. It combines a temporal model of neural dynamics with a spatial model of a layer specific lead field to facilitate layer separation. In simulations we show that the spatial arrangement of the two layers can be successfully recovered using Bayesian inference. The layered model can also be distinguished from a single dipole model. We conclude that precision magnetoencephalography in combination with detailed dynamical system modeling can be used to study non-invasively the fast dynamics of layered computations.

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bioRxiv2019

Estimates of cortical column orientation improveMEG source inversion

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.

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bioRxiv2017

Laminar-specific cortical dynamics in human visual and sensorimotor cortices

Lower frequency, feedback, activity in the alpha and beta range is thought to predominantly originate from infragranular cortical layers, whereas feedforward signals in the gamma range stem largely from supragranular layers. Distinct anatomical and spectral channels may therefore play specialized roles in communication within hierarchical cortical networks; however, empirical evidence for this organization in humans is limited. We leverage high precision MEG to test this proposal, directly and non-invasively, in human participants during visually guided actions. Visual alpha activity mapped onto deep cortical laminae, whereas visual gamma activity predominantly arose from superficial laminae. This laminar-specificity was echoed in sensorimotor beta and gamma activity. Visual gamma activity scaled with task demands in a way compatible with feedforward signaling. For sensorimotor activity, we observed a more complex relationship with feedback and feedforward processes. Distinct frequency channels thus operate in a laminar-specific manner, but with dissociable functional roles across sensory and motor cortices.

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bioRxiv2017

Non-invasive laminar inference with MEG: Comparison of methods and source inversion algorithms

Magnetoencephalography (MEG) is a direct measure of neuronal current flow; its anatomical resolution is therefore not constrained by physiology but rather by data quality and the models used to explain these data. Recent simulation work has shown that it is possible to distinguish between signals arising in the deep and superficial cortical laminae given accurate knowledge of these surfaces with respect to the MEG sensors. This previous work has focused around a single inversion scheme (multiple sparse priors) and a single global parametric fit metric (free energy). In this paper we use several different source inversion algorithms and both local and global, as well as parametric and non-parametric fit metrics in order to demonstrate the robustness of the discrimination between layers. We find that only algorithms with some sparsity constraint can successfully be used to make laminar discrimination. Importantly, local t-statistics, global cross-validation and free energy all provide robust and mutually corroborating metrics of fit. We show that discrimination accuracy is affected by patch size estimates, cortical surface features, and lead field strength, which suggests several possible future improvements to this technique. This study demonstrates the possibility of determining the laminar origin of MEG sensor activity, and thus directly testing theories of human cognition that involve laminar- and frequency- specific mechanisms. This possibility can now be achieved using recent developments in high precision MEG, most notably the use of subject-specific head-casts, which allow for significant increases in data quality and therefore anatomically precise MEG recordings.

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