Mathilde Bonnefond

This study uses interleaved EEG-fMRI to show that gamma-band activity correlates with feature-specific visual signals in both superficial and deep cortical layers, and lower frequencies are linked to feature-unspecific BOLD signals.

The paper investigates anticipatory postural control in children using magnetoencephalography, revealing adult-like neural mechanisms and an additional compensatory mechanism for postural stabilization.

The paper shows that alpha-band activity in V1 carries stimulus information with fine spatial specificity during figure-ground segregation, linking low-frequency rhythms to effective communication between cortical areas.

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

This study uses MEG and machine learning to decode spatial attention in humans during different tasks, showing dynamic, task-dependent attentional patterns similar to those in non-human primates.

This study investigates the role of alpha-beta oscillations in inhibiting the ventral attention network during an attention task, finding increased alpha-beta power in the VAN and correlations with reaction times.

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 study investigates how temporal variability in auditory stimuli affects perception, finding that discrimination accuracy declines with increased temporal variability.

This study investigates the role of the ventral attention network (VAN) and dorsal attention network (DAN) in multimodal attentional reorientation using MEG, finding that alpha/beta oscillations modulate VAN and DAN activity during top-down attention and involuntary attentional capture.

This study examines how EEG power modulations relate to changes in laminar fMRI connectivity during an attentional task, finding distinct effects for alpha and beta bands.

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