Section: Application Domains
Parietal research axes
In order to address these questions, parietal currently develops three main research axes:
Create some tools to understand brain functional architecture, i.e. the relationship between brain structure (anatomy) and its functional organization. For instance, there is currently much interest in modelling the links between anatomical connectivity, characterised through fibre tracts that connect distant regions, and functional connectivity, i.e. the correlation in the activity between distant brain regions across time. In particular, our aim is to extract the main salient brain structures that can be observed in neuroimaging datasets from several subjects. The final aim of this axis is to build atlases of the brain that will be based on multi-modal information (anatomical, functional and diffusion MRI).
The second axis is more classically related to the methodology for group analysis of neuroimaging data based on regression and classification techniques, thus trying to quantify and explain inter-subject differences, in particular when behavioral or genetic information are available to characterize the patients.
The third axis consists in finding some coding schemes that express how the brain processes encode some particular information, either in perception or action context. A very promising approach, called inverse inference , proceeds by predicting mental state from functional neuroimaging data. Moreover, the co-occurrence of signals modulation across regions, called functional connectivity , is a fundamental marker of brain functional organization that complements the description obtained through decoding approaches.
An important motivation for these developments is that the advent of high-field Magnetic Resonance Imaging (MRI) will allow an increase of image resolution and quality which should be used to enhance image understanding and analysis. As a member of Neurospin platform, parietal aims at proposing novel analyzing techniques that will take advantage of the high-quality data.