Thomas Andrillon

This study examines iota oscillations in REM sleep across age groups, finding age-related changes in frequency, amplitude, and bandwidth, with high heritability for most properties.

This study shows that spontaneous slow waves in Disorders of Consciousness patients correlate with recovery potential and residual awareness, challenging the view of slow waves as merely a marker of impaired consciousness.

This study investigates how alpha oscillations relate to mind-wandering and sleepiness, finding opposite effects when eyes are open versus closed.

This study finds no group differences in sustained attention performance or wake slow wave activity between ADHD and neurotypical adults, but identifies neural differences suggesting compensatory mechanisms in ADHD.

This paper introduces a homeostatic inhibitory plasticity rule into a biophysical model to study how local adaptive processes influence global brain dynamics.

This study finds that increased sleep-like slow wave (SW) density during wakefulness in ADHD is associated with attentional difficulties, suggesting a mechanistic link between sleep disturbances and attentional issues.

This study identifies neurophysiological signatures of mind blanking using EEG, showing distinct patterns compared to mind wandering and on-task states.

This study shows that sleep-like slow waves in wakefulness correlate with perceptual decision-making deficits in stroke patients, indicating network destabilization beyond the lesion site.

The paper examines how cardiac activity and brain-heart coupling relate to attentional states and vigilance during a sustained attention task.

This study shows that low-dose ketamine impairs conscious access in healthy participants, correlating with psychotic-like symptoms but not manic-like ones, and affecting early visual processing.

The paper investigates the neural correlates of mind blanking (MB) using EEG-fMRI and experience-sampling, finding that MB is associated with distinct brain connectivity patterns depending on vigilance levels.

CARACAS is an ECG-free tool for identifying cardiac independent components in EEG data, achieving high sensitivity and specificity compared to existing methods.

This study uses dynamical independence to analyze how different anaesthetics affect emergent neural dynamics in the human brain, revealing drug-specific changes in structure and organization.

The paper identifies EEG features distinguishing idiopathic hypersomnia from controls using sleep microarchitecture and hypnodensity metrics.

This paper introduces Dynamical Independence (DI) to identify emergent macroscopic dynamics in a biophysical neural model by analyzing the balance between integration and segregation.

This paper detects sleep deprivation using voice analysis with machine learning, identifying two distinct vocal biomarkers: prosody and timbre changes, which vary individually.

This study investigates neural responses to interoceptive and exteroceptive attention using brain dynamics and HEP features, showing distinct patterns that can classify attentional states and assess command-following in brain-injured patients.

The paper shows that N1 sleep leads to more memory forgetting compared to N2 sleep, with alpha/theta power ratio linked to forgetting rates.

This study investigates how pharmacological manipulations of arousal and sleep-like slow waves affect sustained attention in humans.

This study shows that the pupillary light response correlates with sensory imagery strength and is absent in aphantasia, providing physiological validation of the condition.

The paper investigates how prior expectations improve speech comprehension in noisy conditions by analyzing EEG and pupillometry data.

This paper introduces a framework using over 7700 time-series features to cluster sleep epochs, revealing both overlaps and discrepancies with traditional visual scoring methods.

The paper identifies sleep-like slow waves in the awake brain associated with attentional lapses, mind wandering, and mind blanking through behavioral and neural analysis.

The paper introduces a method to interpret machine-learning classifiers by analyzing how noise and reverse correlation affect classification decisions across various data types and models.