Thesis Clément GUICHET

The integration of multimodal data to decipher the neural processes and networks underlying cognition and behavior is a major challenge in cognitive neuroscience. This is associated with a current paradigm shift with neurocognitive models considering that human behaviors are enabled by complex interactions between cognitive functions. The fusion (integration) of multimodal data could not only compensate for the limitations of each modality, but also detect features that are intrinsically multimodal.
Objectives and research program. This research work is at crossroads between language and declarative memory and addresses the question of their interactive union in a multimodal and integrative perspective, with artificial intelligence methods. The project will have two dimensions, neurocognitive and neurocomputational. On the neurocognitive level, the objective is to validate and enrich this new theoretical framework LuM (language-union-memory) that we have recently developed (Roger et al., 2022) and which consists in considering that language and memory are two inseparable functions and that their evaluation must be done in an interactive joint way, rather than in isolation. The project will also have a transcognitive dimension and try to explore this framework in normal and pathological settings. On the neuro-computational side, we will use several analysis methods (unsupervised machine learning, graph theory and deep learning) for multimodal fusion and develop transfer learning in the framework of deep learning AI. Multimodal biomarkers (neuropsychology, functional and anatomical neuroimaging) have been acquired in our previous work.

Supervisors :
Monica BACIU Monica.Baciuuniv-grenoble-alpes.fr (Monica[dot]Baciu[at]univ-grenoble-alpes[dot]fr)
Martial MERMILLOD martial.mermilloduniv-grenoble-alpes.fr (martial[dot]mermillod[at]univ-grenoble-alpes[dot]fr) (Codirection)

Keywords : Language,Memory,Cross-cognitive Modeling,Cognitive Neuroscience,Computational Neuroscience,