Neural and Computational Bases of Human Learning
We have shown that associative visuomotor learning involves the activation of cortico-striatal networks for the processing of outcomes (Brovelli et al., 2008) and choices (Brovelli et al., 2011). More recently, in collaboration with Benoit Girard and Mehdi Khamassi from ISIR, we have developed a dual computational approach modelling choices and reaction time during arbitrary visuomotor learning through the coordination of adaptive working memory and reinforcement learning (Viejo et al., 2015). Ovearll, the goal of our projects is to investigate how information flows in fronto-striatal cortico-cortical and cortico-striatal circuits during reward-based learning. In collaboration with Paul Apicella and Ruggero Basanisi at INT, we also studying how beliefs about causal relations between actions and outcomes form during goal-directed learning. These projects are funded by the the Mission pour l’Interdisciplinarité of the CNRS GoHal project (2012-2013), by the Integrative and Clinical Neuroscience PhD program through a PhD fellowship awarded to Ruggero Basanisi (2018-2021). Since 2018, I coordinate an ANR project (CausaL – Cognitive architectures of causal learning) which aims at studying The aim of this project is to study the neural and computational bases of human goal-directed causal learning by combining human neurophysiology (MEG and intracranial SEEG) and neuroimaging (fMRI) techniques with computational models of learning (Reinforcement Learning and Active Inference). The consortium includes Mateus Joffily (GATE, Lyon), Mehdi Khamassi (ISIR, Paris), Julien Bastin (GIN, Grenoble).
Cognitive Brain Networks Discovery
Previous work has shown that Granger causality is an effective tools for the analysis of directional statistical dependences between neural signals (Brovelli et al., 2004). More recently, we have developed approaches for the analysis of single-trial Granger causality (Brovelli et al., 2015) and the dynamics of functional connectivity, i.e., temporal brain networks (Brovelli et al., 2017). Current FC tools are based on a cortical parcellation atlas developed in collaboration with the MeCa team (Auzias et al., 2016). We are currently developping automatised pipelines for the analysis of task-related Functional Connectivity Dynamics (FCD) in collaboration with Demian Battaglia. These projects are supported by the Institute of Language, Communication and the Brain and the Mission pour l’Interdisciplinarité of the CNRS BrainTime project (2017-2019). Since 2020, I coordinate a research project (NetScovery) within the WP1 of the Human Brain Project, which aims at combining data-driven and model-based approaches for the inference of brain connectivity and the validation of whole-brain models. The goal is to integrate workflows and methods into EBRAINS. The consortium includes Jean Daunizeau (ICM, Paris), Gustavo Deco (UPF, Barcelona), Stefano Panzeri (IIT, Italy), Petra Ritter (Charité, Berlin) and Olivier David (GIN, Grenoble).
Functional Connectivity Alterations of Resting-State Networks
Since 2018, I am co-PI of a resaerch project (Brainsynch-Hit) aiming at studying directional interactions in resting-state brain networks and their ability in predicting cognitive deficits observed post-stroke in patients. In collaboration with Maurizio Corbetta (Padova Univ), Rainer Goebel (Maastricht) and Gustavo Deco. Brainsynch-Hit project (2018-2020) funded by the FLAG-ERA FET Flagship (Human Brain Project)
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