The main objective of my research is to study pain - or nociception- induced central nervous system plasticity. Currently, my studies are focused on characterizing changes in nociceptive pathways that lead or contribute to hyperalgesia, and I am, thereby, particularly interested in the phenomenon of "central sensitization"; the increase responsiveness of nociceptive neurons in the central nervous system that generates widespread hyperalgesia ("secondary hyperalgesia").

How the perception of pain emerges from brain activity remains elusive. Pain perception and associated brain responses are known to be highly sensitive to several contextual, cognitive and mood factors. In particular, prior expectations largely influence pain, as exemplified by powerful placebo and nocebo effects. These effects suggest that pain perception does not result from a direct readout of sensory inputs, but can be seen as the outcome of an inference process based on noisy observations, which necessitates an internal model accounting for endogenous modulations. Yet, there is no unifying framework explaining how prior expectations and incoming stimuli are combined in our brain to lead to pain perception. In this setting, this project aims to shed light on the mechanisms governing the construction and updating of internal pain models. To reach this goal, we will first develop statistical models that can explain how human subjects combine prior information with incoming evidences to identify which inferential features are encoded in brain responses. Second, in order to understand how neuronal circuits can encode these statistical features, we will design artificial neural networks that replicate human performance in pain inference tasks, and analyze how their learned structures help predicting future stimulus intensities. Both families of models will be adapted to the studied context, and assessed on several already recorded electrophysiological datasets. By analyzing the learned reasonings in machine learning models, we aim, on the one hand, to study their artificial learning mechanisms and, on the other hand, to enhance our understanding of biological pain networks.

People with chronic pain can have distorted cognitive representations of their painful body part and its surroundings. These problems affect patients’ abilities to normally perceive and act in their environment with the painful body part, and can worsen their symptoms. Looking into the brain mechanisms of specific cognitive difficulties can help us better understand how they may contribute to the clinical symptoms. My project investigates how patients process the information close to their painful body part by using behavioural and virtual manipulations and recording their brain responses. I am also testing a novel rehabilitation method using virtual reality to manipulate what patients see when they make reaching movements with their painful arm, and make them learn to flexibly adapt their movements to the changing environment. Demonstrating whether such intervention can alleviate patients’ pain would support integrating broader neuropsychological rehabilitation methods with classic medical interventions for effective pain management.

As a neurorehabilitation clinician, I am in charge of a care unit for patients with prolonged disorder of consciousness (i.e. patients with unresponsive wakefulness syndrome or in minimally conscious state). The aim of my research project is to understand how these severely brain injured patients are able to perceive pain using non-invasive methods such as surface EEG recordings. These investigations should also be of interest to develop a novel approach to study the relationship between pain perception and consciousness.

Studies have suggested that Alzheimer's disease (AD) is related to changes in brain function that are present already at very early, pre-clinical stages of the disease. For example, recent functional neuroimaging studies have shown early alterations in brain connectivity, and that these alterations are most prominent in highly-connected cortical "hub areas". These hub areas are also those that are most affected by AD lesions. These findings support the view that AD pathology could, at least in part, result from an activity-dependent degeneration. Initial excessive neural firing in hub areas due to increased excitability or connectivity could lead to later neurodegeneration and disruption of connectivity. Very recently, studies conducted by Prof. JN Octave (UCL) have suggested that AD could be related to a decrease in the expression of the cellular Cl- ion extruder KCC2, leading to an increase in intracellular Cl- and, thereby, an inhibitory-to-excitatory shift of GABAA receptor activity. The aim of the present study is to test whether GABAergic neurotransmission is altered at early pre-clinical and pre-demential stages of AD as compared to matched healthy controls.