Renaud Jardri

The paper analyzes fMRI data to identify brain networks involved in radio speech listening and hallucination experiences in schizophrenia, finding speech-duration-dependent responses but not hallucination-duration-dependent ones.

The paper develops an fMRI-based method to detect auditory hallucinations in schizophrenia patients using a linear classifier, achieving high decoding accuracy across independent datasets.

The paper predicts individual schizophrenia symptom dimensions using functional brain networks and links these networks to molecular features like dopamine and serotonin systems.

This study examines how treatment response in PTSD alters effective connectivity of the anterior insula, linking changes to intrusive symptom severity and network stability.

The paper investigates bistable perception using a Bayesian framework and finds that a circular inference model better explains the data than optimal Bayesian integration.

The paper uses machine learning to rank cognitive domains based on brain data to predict schizophrenia in a multi-site dataset.