Luiz Pessoa

The paper analyzes resting-state fMRI data to determine whether brain state transitions are discrete or continuous, finding structured and heterogeneous transitions that depend on anatomical regions.

The paper uses Bayesian generative modeling to show the mouse brain's functional connectome has multiple co-dominant organizational configurations rather than a single structure.

The paper investigates how value processing differs from arousal and valence in human approach-avoidance behaviors using fMRI and identifies independent neural correlates of value.

This paper applies Switching Linear Dynamical Systems to analyze fMRI data during a threat-of-shock paradigm, revealing dynamic brain patterns and their contributions to threat processing.

The paper uses Ca2+ imaging and fMRI-BOLD in mice to compare functional network organization, finding overlapping but distinct patterns between modalities.

This study investigates the dynamics of aversive and appetitive processing in the human brain during threat-avoidance and reward-seeking tasks, revealing temporally increasing responses in multiple brain regions.

The paper identifies information loss sources in neuroimaging and proposes a Bayesian framework to improve inference and reproducibility.

This study examines how sustained threat is processed in the human brain using fMRI, finding widespread neural effects beyond traditional emotion-related regions.

This paper introduces a recurrent neural network-based framework to decode and predict individual differences in brain dynamics using fMRI data from movie-watching and experimental conditions.

The paper investigates how controllability over stressors affects brain responses to a shock-plus-sound stressor, finding decreased responses in key threat-related regions with controllability.

This paper introduces a Bayesian multilevel model framework to account for cross-trial variability in fMRI data, showing improved statistical sensitivity and accuracy compared to traditional methods.

The paper introduces an LSTM-based framework to analyze spatiotemporal brain dynamics from fMRI data, showing it can classify movie stimuli and predict individual traits.

This study investigates how emotion and action interact in the brain by examining context-dependent responses to threatening stimuli using fMRI and Bayesian analysis.

This paper introduces a geodesic distance metric to compare functional connectivity matrices, achieving high participant identification accuracy and outperforming traditional methods.

The paper proposes a Bayesian multilevel approach to address multiple testing in neuroimaging by embracing multiplicity through global calibration, aiming to improve model efficiency and reproducibility.

This study uses fMRI and representational similarity analysis to examine how brain regions organize novel task encoding based on control-related variables and reward expectations.

The paper introduces a Bayesian multilevel modeling approach for neuroimaging connectomics that integrates region, region pairs, and subject analyses to improve statistical inference and avoid multiple testing issues.

This study investigates how proximity, direction, and speed interact to influence threat processing in the human brain using fMRI and skin conductance responses.

The paper introduces reservoir computing for analyzing dynamic fMRI data, showing it can classify tasks and extract low-dimensional temporal trajectories with high accuracy.

This study investigates how large-scale brain networks reorganize during dynamic anxious anticipation using functional MRI and intersubject network analysis, finding changes in network cohesion and functional connections during approach and retreat phases.