Team parietal

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Section: Application Domains

Parietal research axes

In order to address the above questions, parietal currently develops three main research axes:

  1. 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, characterized through fibre tracts that connect distant regions, and functional connectivity, i.e. the correlation in the activity between distant brain regions across time.

    This involves the accurate definition of structures of interest in either modality and the coregistration of such structures across individuals.

    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) without ignoring between-subject differences.

  2. 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.

    This involves the use of sophisticated statistical inference and machine learning tools.

  3. 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.


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