Sylvain Baillet

This paper introduces an autoencoder framework to derive individualized neurophysiological profiles from resting-state MEG data without participant labels, achieving high accuracy in differentiation and prediction.

sEEG-Suite provides a semi-automated pipeline for sEEG contact localization and anatomical labeling within Brainstorm, improving accuracy and efficiency in epileptic network mapping.

The paper examines how fiber-tract development in the human hippocampus correlates with functional specialization across childhood, finding that cross-sectional area of specific tracts predicts functional specialization.

This paper introduces geodesic cortical flow to quantify spatiotemporal propagation patterns in resting-state MEG data from healthy adults, revealing frequency-specific directional biases and cognitive associations.

This study identifies reduced beta-band neural modulation as a shared mechanism linking perceptual, motor, and language impairments in schizophrenia.

This study uses MEG to show that human motor areas exhibit RPE-like signals during deterministic tasks, supporting embodied decision-making.

This study examines beta-burst dynamics in schizophrenia patients and controls, finding dissociable anterior and posterior abnormalities linked to speech markers of thought disorder.

The study examines how neurophysiological traits evolve across the lifespan using MEG data from 1,000 individuals, finding age-related shifts in brain regions associated with individuality and genetic influences.

The paper examines how changes in functional connectivity during rest and task relate to Alzheimer's pathology and episodic memory in older adults.

The paper estimates lower bounds of electrophysiological power dissipation in human gray matter using MEG data, finding it significantly lower than metabolic estimates.

This study investigates the genetic basis of neurophysiological variability using twin and unrelated individuals, finding significant genetic influence on brain activity traits linked to neurotransmission and cortical gene expression patterns.

This study investigates how microsaccades prevent perceptual filling-in during fixation, finding that color contrast and retinal eccentricity affect this process differently.

This paper introduces a Bayesian model-selection method to automatically determine the number of spectral peaks in neural power spectra, improving reproducibility and robustness.

This study maps neurophysiological dynamics to cortical micro-architecture, finding spatial correlations between brain activity patterns and microstructural features.

The paper quantifies structure-function coupling in brain networks using diffusion MRI, MEG, and fMRI, finding heterogeneity across regions and frequency bands.

A preregistered multimodal neuroimaging study across 256 human participants directly tests Integrated Information Theory against Global Neuronal Workspace Theory, confirming some predictions while revealing major empirical challenges for both.

The paper investigates premotor cortex activity during passive observation of rule-based stimuli, finding activation related to abstract rule evaluation even without motor actions.

This study investigates how exogenous attention interferes with endogenous attention through alpha-band neural activity, using a moderated mediation model to show the mediating role of alpha power in attentional interference.

Neurotransmitter receptor/transporter PET maps (19 targets, 1239 healthy participants) align with connectomes, explain MEG power, and track cognitive gradients and cortical thinning in 13 disorders.

Neuromaps provides a software package to standardize and compare brain maps across different coordinate systems, enabling comprehensive annotation analysis of the human brain.

This study examines age-related changes in deep-brain neurophysiological activity using resting-state MEG data from healthy young and older volunteers.

This paper introduces SPRiNT, a method to time-resolve aperiodic and periodic neural activity, showing its effectiveness on synthetic and empirical data.

This study maps electromagnetic and haemodynamic networks in the brain, finding they align in unimodal cortex but diverge in transmodal cortex, driven by cytoarchitectural factors.

The paper investigates how endogenous attention affects exogenous attention processing and shows that alpha-band brain activity patterns change when both types of attention are engaged.

This study determines the minimal data duration needed for robust spectral estimates in resting-state MEG, showing that 30-120 seconds of data are sufficient across different recording conditions and populations.

This study uses MEG to track perisaccadic visual signals, revealing beta-band activity propagating from parietal to visual cortex, indicating remapping of visual signals during eye movements.

The paper investigates oscillatory entrainment in auditory cortical and subcortical structures using FFR data from MEG and EEG, finding evidence of phase alignment and frequency convergence.

This study identifies individuals using resting-state MEG data, showing high identifiability rates and robustness over time.

This study investigates brain network dynamics during pitch change detection, showing slow oscillations and phase-amplitude coupling in auditory and motor regions, with differences in amusia.

The paper shows that variability in stimulus-induced gamma responses is linked to spontaneous brain network states, with both trial-to-trial and subject-to-subject differences explained by pre-stimulus network dynamics.