Diego Vidaurre

Whole-brain normative modeling across multiple functional connectivity metrics in MEG

This paper introduces M/EEG-FUNCOIN, a normative modeling approach for whole-brain functional connectivity in MEG data, showing improved characterization of age and sex-related patterns and detection of connectivity deviations linked to cognitive ability.

What drives apparent dynamic functional connectivity in resting-state fMRI?

This study investigates the drivers of dynamic functional connectivity in resting-state fMRI using HMMs and finds that mean activity and variance dynamics explain most of the apparent connectivity changes.

Multimodal autonomic arousal tracks dose-dependent affective dynamics during the acute effects of DMT

This study tracks dose-dependent affective dynamics during DMT's acute effects using multimodal physiological signals and phenomenological reports.

A spatially-specific pattern of cortical thickness in frontal lobe predisposes to faster future amyloid accumulation in individuals with subjective cognitive decline

The paper identifies a frontal lobe cortical thickness pattern in amyloid-negative individuals that predicts faster amyloid accumulation over five years.

Anterior default mode brain state dynamics predict depressive symptom severity before and during TMS treatment

The paper uses EEG data from TMS-treated patients to show that changes in anterior default mode brain state dynamics predict depressive symptom improvement during treatment.

Test-retest reliable and site-robust Hidden Markov Model framework for discovering whole-brain beta activity

This paper introduces a Hidden Markov Model (HMM) framework to automatically segment beta activity from MEG data, showing good test-retest reliability and multisite transferability.

Effects of age on resting-state cortical networks

This study examines how age affects resting-state cortical networks using MEG data from 612 healthy adults, finding age-related changes in oscillatory power and coherence, and dynamic network dynamics.

Normative modelling of brain function abnormalities in complex pathology needs the whole brain

The paper introduces FUNCOIN, a whole-brain normative model for functional connectivity, and shows it detects abnormal patterns in bipolar disorder and Parkinson's disease better than localized models.

The role of age in the relationship between brain structure and cognition: moderator or confound?

The paper examines how age affects the relationship between brain structure and cognition, finding that models trained on younger subjects predict cognition in older subjects better than vice versa.

Canonical Hidden Markov Model Networks for Studying M/EEG

The paper introduces canonical Hidden Markov Models (HMMs) for M/EEG data, enabling consistent network analysis across studies by using pre-trained models instead of training new ones for each dataset.

Prediction of individual melodic contour processing in sensory association cortices from resting state functional connectivity

The paper predicts individual differences in melodic contour processing from resting state functional connectivity, finding higher predictability in sensory association cortices.

Time- and frequency-resolved decomposition of beta brain activity reveals two functionally distinct beta bands

This study identifies two distinct beta bands in brain activity using MEG and a Hidden Markov Model, showing different roles in movement initiation, maintenance, and termination.

Adaptive recruitment of cortex-wide recurrence for visual object recognition

The paper tests the adaptive recurrence hypothesis by comparing fMRI and EEG responses to images recognized by humans but differing in their solvability by a feedforward deep neural network, finding that parieto-frontal networks are recruited when ventral stream processing is insufficient.

Spontaneous network transitions predict somatosensory perception

The paper shows that spontaneous brain network transitions during the pre-stimulus period predict correct or incorrect somatosensory perception in an ambiguous tactile task.

The link between gender inequality and the distribution of brain regions' relative sizes across the lifespan and the world

The paper examines how gender inequality affects brain grey matter differences between males and females across age and geography, finding a cumulative effect with age and economic factors dominating the influence.

Weakened prefrontal activation dynamics associated with slowed information processing speed in multiple sclerosis

This study uses MEG data and a data-driven method to identify task-specific brain states linked to information processing speed in multiple sclerosis, finding reduced prefrontal activation in patients.

Targeted Time-Varying Functional Connectivity

This paper introduces T-TVFC, a method for targeted time-varying functional connectivity analysis, and shows it captures task-related information in thalamocortical networks not found in traditional methods.

Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel

The paper introduces an HMM-Fisher kernel approach to predict individual traits from brain dynamics using fMRI data, showing it is accurate and reliable compared to other methods.

Predicting subject traits from brain spectral signatures: an application to brain ageing

The paper predicts biological age from EEG data using kernel methods, showing improved performance over standard approaches.

Frequency-specific theta states in the hippocampus are linked to reconfiguration of population activity with respect to behavioural context

The paper identifies two theta oscillation states in rats during an odor-memory task and links them to changes in hippocampal population activity related to trial outcomes.

Impaired activation of the prefrontal executive network during working memory processing in multiple sclerosis

This paper uses TDE-HMM to analyze MEG data from a working memory task in MS patients, identifying altered activation patterns in specific brain networks linked to cognitive impairment and treatment effects.

Modelling of brain dynamics reveals reduced switching between brain states in insomnia disorder - a resting state fMRI study

Stacking models of brain dynamics improves prediction of subject traits in fMRI

The paper improves prediction of cognitive traits from fMRI data by stacking multiple dynamic brain network models to reduce variability and enhance accuracy.

A novel description of the network dynamics underpinning working memory

This paper uses TDE-HMM to analyze MEG data and identifies four task-specific brain states associated with working memory processes.

osl-dynamics: A toolbox for modelling fast dynamic brain activity

osl-dynamics is a Python toolbox that models fast brain dynamics in neuroimaging data using machine learning, providing tools for analysis and visualization of oscillatory and network activity.

Large-scale cortical networks are organised in structured cycles

The paper shows that large-scale cortical networks exhibit cyclical activity patterns across multiple MEG datasets, with implications for cognitive function and behavior.

Dissecting unsupervised learning through hidden Markov modelling in electrophysiological data

The paper evaluates how different assumptions in hidden Markov models affect state decomposition in electrophysiological data using synthetic and real data.

Differential activity patterns in the upper and lower blade of the dentate gyrus

The paper compares activity patterns in the upper and lower blades of the dentate gyrus during a decision-making task, finding distinct roles for each blade in encoding emotional and actual responses.

A generative model of electrophysiological brain responses to stimulation: an approach to study perceptual stability

The paper introduces a generative model of brain responses to stimulation, showing that stable decoding features involve oscillatory and slower components with specific phase relations.

Towards stability of dynamic FC estimates in neuroimaging and electrophysiology: solutions and limits

The paper improves the stability of time-varying functional connectivity estimates using Hidden Markov Models and hierarchical clustering on fMRI and MEG data.