Diego Vidaurre

This paper identifies a frequency doubling artifact in decoding accuracy metrics from neurophysiological data and proposes a filtering approach and alternative decoding method to mitigate it.

This paper introduces STRM, a method for analyzing M/EEG data to infer spatiotemporal activity patterns and improve predictive accuracy in neuroscience tasks.

This paper investigates how data characteristics and model parameters affect the ability of time-varying functional connectivity models to detect temporal changes in fMRI data.

This paper introduces a multi-dynamic model to separately analyze time-varying brain activity and functional connectivity in resting fMRI, revealing stronger temporal changes in connectivity and better prediction of behavioral variability.

The paper shows how theta frequency modulation enables selective information routing in brain networks, which is impaired in Alzheimer's disease models.

This study shows that brain stimulation enhances perceptual learning by reducing GABA levels and altering functional connectivity between visual and decision-related brain areas.

This paper investigates whether fMRI functional connectivity reflects dynamic neural communication by developing and applying PCCA to show context-dependent, cognitively relevant changes in connectivity trajectories.

This study examines how dopaminergic medication affects oscillatory brain networks in Parkinson's disease using combined STN and MEG recordings.

This paper addresses the statistical challenge of reliably measuring time-varying functional connectivity in high-dimensional fMRI data by proposing a novel method that integrates dimensionality reduction and FC estimation.

The paper links individual variations in mental health to deviations in dynamic brain states associated with cognitive ability using UK Biobank data.

The paper develops methods to assess time-varying functional connectivity in fMRI and shows it explains behavioural traits not captured by structural data or time-averaged connectivity.

The paper shows replay bursts occur during activation of the default mode and parietal alpha networks, coordinating information flow between cortical areas.

This study identifies two distinct neural states during wakeful rest using machine learning and links them to specific psychological features like problem-solving and negative rumination.

This paper uses a 3D CNN to predict brain age from MRI data and finds that prediction errors correlate with clinical measurements and image-derived phenotypes.

The paper identifies three dissociable components of oscillatory activity in MEG data that underlie information encoding in human perception.

The paper provides direct evidence of reversed visual hierarchy activation during mental imagery and reports an 11Hz oscillatory pattern during perception.

The paper predicts individual task responses from resting-state MEG transient spectral events across multiple tasks and subjects.

The paper evaluates and optimizes network modelling methods for fMRI by testing various parcellation, connectivity measures, and classifiers across multiple datasets to find the best predictive pipeline.

This study shows that effective cognitive therapy for PTSD restores balance in brain networks involved in self-referential processing and threat detection.

The paper identifies an invariant global workspace in the human brain using a normalized directed transfer entropy framework and multimodal neuroimaging data.

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.

The paper evaluates methods for estimating brain age delta from brain imaging data and identifies variables strongly correlated with it using UK Biobank data.

This study introduces a temporally unconstrained decoding method to track trial-specific neural dynamics in stimulus processing and decision making, revealing consistent but time-varying processing stages.

This study links neural state transitions during wakeful rest to patterns of ongoing thought and well-being.

The paper proposes a permutation testing method to improve statistical inference from noisy functional connectivity estimates by combining results across replications.

The paper identifies frequency-specific phase-coupling networks in resting-state magnetoencephalography data, revealing distinct brain states associated with sensory, motor, and higher-order cognitive networks.

The paper uses magnetoencephalography and Hidden Markov Modelling to detect changes in {beta}-activity during motor learning, showing its relevance across different motor tasks.

The paper examines how brain network properties at birth predict language skills and shows that syntactic abilities are affected by preterm birth.

The paper describes an automated processing and quality control pipeline for brain imaging data from the first 10,000 UK Biobank participants.