James John Bonaiuto

This study investigates sensorimotor beta bursts as a biomarker for graded force modulation in humans, finding that burst features correlate with force levels during motor execution.

The paper investigates how beta bursts in the supplementary motor area (SMA) are linked to anticipatory muscle inhibition during voluntary load-lifting.

The paper analyzes beta burst dynamics in children with and without developmental coordination disorder to show that waveform-specific burst rates correlate with motor performance rather than diagnostic status.

The paper uses deformable cortical surfaces to evaluate MEG source reconstruction algorithms and fit metrics.

The paper develops a beta burst extraction pipeline using Helium-OPMs to study motor preparation and execution, finding similar waveform patterns to SQUID-MEG with task-related modulations.

This paper introduces a multilayer MEG source modelling framework to enable depth-resolved laminar inference in humans, showing it aligns with known neural circuit motifs.

This paper introduces a new method, lagged Hilbert autocoherence (LHaC), to better quantify neural rhythmicity compared to existing Fourier-based methods.

The paper investigates beta burst activity in motor imagery tasks, showing that burst-based features can match or exceed traditional beta power methods in decoding brain signals.

This paper improves motor imagery-based BCI by analyzing beta burst waveforms instead of beta power, showing better classification performance and information transfer rates.

The paper examines how sensorimotor beta burst activity develops from infancy to adulthood using EEG recordings and a novel analysis method.

This paper introduces a multilevel Bayesian HMM to analyze longitudinal neural recordings, identifying stable latent states linked to behavioral events in macaque motor cortex.

The paper identifies diverse beta burst waveform motifs in human MEG data during movement tasks and shows they reflect distinct computational processes.

This study identifies spatiotemporal wave-like patterns of beta bursts in human sensorimotor cortex using magnetoencephalography, showing distinct propagation directions with different anatomical and physiological features.

The paper uses high precision MEG to show R-IFG beta power increases during response conflict, suggesting a generalized role in response inhibition beyond simple stopping.

This study identifies laminar dynamics of beta bursts in human motor cortex using high precision MEG and a neocortical circuit model, showing distinct excitatory inputs to deep and superficial layers.

This study uses dual-coil TMS to map effective connectivity between frontal cortex regions and contralateral M1, identifying task-specific functional subdivisions.

This paper presents a dynamic causal model of a two-layered cortical microcircuit using magnetoencephalography to study non-invasive layered computations.

This study improves MEG source inversion by using white matter surface locations and correspondence-based orientation estimation, showing better performance in evoked response fitting.

The paper investigates transient beta bursts in motor cortex and finds they predict movement onset and delay after errors, suggesting roles in goal specification and error monitoring.

The paper evaluates MEG source reconstruction methods using resting state data, finding that the Empirical Bayesian Beamformer provides the best anatomical discrimination.

This paper compares MEG source inversion algorithms and fit metrics to determine the laminar origin of neural activity, finding that sparsity-constrained methods and specific fit metrics improve discrimination accuracy.

The paper uses MEG to show laminar-specific frequency activity in human visual and sensorimotor cortices, with distinct roles for alpha/beta and gamma bands.