Matthieu Gilson

Towards a scalable effective connectivity estimation in fMRI

This paper introduces a scalable method for estimating effective connectivity in fMRI data, showing it matches advanced DCM results and outperforms functional connectivity for connectome fingerprinting.

Functional and Structural Brain Age Gaps as Complementary Predictive Markers for Cognitive Performance

The paper predicts structural and functional brain age gaps from MRI data to assess their predictive power for cognitive performance in healthy adults.

SK2/3 CHANNELS COUPLE WITH T-TYPE CA2+ CHANNELS TO GATE SPINAL LOCOMOTOR RHYTHM GENERATION

The paper identifies SK2/3 channels coupled with T-type Ca2+ channels as a mechanism gating spinal locomotor rhythm generation, showing their role in initiating and terminating locomotor activity.

Dual temporal codes for voice identity in the primate auditory cortex

The paper investigates vocal identity encoding in primate auditory cortex by recording single-neuron activity in rhesus macaques and identifies two distinct temporal coding strategies for voice recognition.

A comparative study between a power and a connectivity sEEG biomarker for seizure-onset zone identification in temporal lobe epilepsy

This study compares power and connectivity-based sEEG biomarkers for identifying seizure-onset zones in temporal lobe epilepsy, finding that power-based methods generally perform better but both can achieve similar results with optimal parameter tuning.

Directed neural interactions in fMRI: a comparison between Granger Causality and Effective Connectivity

The paper compares Granger Causality and Effective Connectivity methods for inferring directed neural interactions in fMRI data, showing their mathematical relationship and limitations.

Phase-dependent stimulation response is shaped by the brain's transient collective dynamics.

This study shows that brain stimulation effects depend on dynamic states and phases, with machine learning improving prediction by 40%.

Meditation-induced effects on whole-brain structural and effective connectivity

This study examines meditation's effects on brain structure and function using MRI, finding that effective connectivity features better distinguish meditators from controls than structural or functional connectivity alone.

Posterior integration and thalamo-frontotemporal broadcasting are impaired in disorders of consciousness

This study identifies impaired neural propagation and responsiveness in disorders of consciousness using resting-state fMRI and links these deficits to glucose metabolism and specific circuit failures.

Covariance-based information processing in reservoir computing systems

This paper demonstrates that covariance-based perceptrons outperform mean-based methods in classifying synthetic time series and speech digits by leveraging second-order statistics in reservoir computing.

The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks

The paper introduces a new classification framework for time series data using covariance patterns in recurrent neuronal networks, showing it can effectively capture second-order statistical features.

Propagation of BOLD activity reveals task-dependent directed interactions across human visual cortex

This study uses BOLD activity propagation analysis to show task-dependent changes in directed interactions and cortical excitability across human visual cortex.

Model-based whole-brain effective connectivity to study distributed cognition in health and disease

This paper introduces MOU-EC, a whole-brain effective connectivity model for fMRI data to study brain cognition in health and disease, showing its application in extracting task-specific biomarkers and interpreting connectivity changes.

Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability

This paper introduces a dynamic network framework for analyzing whole-brain fMRI data, showing functional communities merge over time and networks shift from segregated to integrated activity.

Comparing Task-Relevant Information Across Different Methods of Extracting Functional Connectivity

The paper compares functional connectivity measures for brain state classification across cognitive tasks and finds that different measures yield distinct and uncorrelated informative links.

Distinct Modes of Functional Connectivity induced by Movie-Watching

This study identifies a distinct functional connectivity mode during movie-watching that reflects individual differences and is stable over time.

Extracting orthogonal subject- and behavior-specificsignatures from fMRI data using whole-brain effectiveconnectivity

This paper extracts subject- and behavior-specific signatures from fMRI data using effective connectivity, showing they are topologically orthogonal and useful for individualized applications.

Stereotypical Modulations In Dynamic Functional Connectivity Explained By Changes In BOLD Variance

This study examines how fluctuations in functional connectivity and BOLD variance relate to dynamic changes in resting state networks, finding that these fluctuations are not random but reflect non-stationary processes.

Task-related effective connectivity reveals that the cortical rich club gates cortex-wide communication

The paper shows that the cortical rich club increases outgoing effective connectivity during tasks, enabling fast communication with specific peripheral regions while maintaining constant incoming connectivity.

Analysis of fMRI data using noise-diffusion network models: a new covariance-coding perspective

The paper analyzes fMRI data using noise-diffusion network models to assess how changes in brain excitability and coordination are reflected in estimated network models.

Effective connectivity inferred from fMRI transition dynamics during movie viewing points to a balanced reconfiguration of cortical interactions

The paper uses fMRI data from movie watching to infer effective connectivity, finding that task-related changes affect local activity more than connectivity.

Resting State Networks in empirical and simulated dynamic functional connectivity

This study uses tensor decomposition and a mean field model to show stationary dynamics can explain resting state networks in both empirical and simulated data.

Non-parametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data

This paper introduces a non-parametric test for detecting directed connectivity in multivariate autoregressive networks and applies it to multiunit activity data from monkeys.

Exploring Anti-correlated Resting State BOLD Signals Through Dynamic Functional Connectivity and Whole-brain Computational Modeling

The paper analyzes anti-correlated resting-state BOLD signals using dynamic functional connectivity and whole-brain modeling, finding hemodynamic response function drives fluctuations in connectivity and anti-correlations.