The Parietal team focuses on mathematical
methods for statistical inference based on neuroimaging data, with a particular
reliance on machine learning techniques and applications of human functional
imaging. This general theme splits into the following research axes:
- Mathematical methods for multi-modal
brain atlases are a means to expose the information available from images as a
template informed by different observation modalities, and to account for the
standard variability around this template. The Parietal team focuses on
improving the underlying estimation procedures, and developing new markers, such
as those derived from functional connectivity models.
- Statistical analysis for
high-dimensional data addresses the variability of brain structure and function
in a statistical perspective. Parietal aims at contributing statistical models
to detect populations effects with enough sensitivity by exploring recent (e.g.
random forests) or novel (multi-task learning) statistical methodologies.
- Modeling brain function through
neuroimaging addresses another key question, namely the modeling and understanding of brain function based
on functional imaging measurements. Parietal’s contribution to encoding models
will address the particular case of vision, a system which can be observed
accurately with MRI, given its size and the resolution achieved with current MRI
scanners
- One of the major challenges in
encoding, i.e. the modeling of brain function through neuroimaging is the
neuro-vascular coupling which is inherent to the BOLD signal. This has been
addressed in the so-called Joint Detection Estimation framework, which is
continuously improved, both on the computational and modeling size, e.g. with
the inclusion of spatial models and new imaging modalities.
- Parallel MRI acquisitions techniques
with multiple coils have emerged as fast powerful imaging methods, in which the
image data is reconstructed based on partial acquisition of the 2D or 3D Fourier
transform of the image or volume, respectively. The next step being to unify
SENSE and compressive sensing approaches to gain additional acceleration, our
aim is to achieve high-performance acquisitions to optimize the use of scanning
time without loss of quality.
Parietal is also strongly involved in
open-source software development in scientific Python (machine learning) and for
neuroimaging applications. Specifically, Parietal is the main contributor of the
Scikit-Learn, mayavi, Nilearn and PyHRF free software.
See https://team.inria.fr/parietal/