PDF version:
http://www.aviz.fr/wiki/uploads/Research/2014_AVIZ_PhD_Project_-_BrainVis.pdfapplication deadline: September 15, 2014
starting date: end of 2014 at the latest
Description:
The study of brain connectivity is one of the fundamental ways to
investigate the complex functions of the (human) brain. For this purpose,
neuroanatomists capture and investigate two different types of
connectivity: anatomical connectivity arising from diffusion-weighted MRI
measurements and functional connectivity based on fMRI scans. Both types of
data have advantages and disadvantages, but ultimately it is essential to
study them in concert.
The research project:
The research in this PhD project will employ and combine approaches from two
sub-fields of visualization: the visualization of spatial relationships
(SciVis, for anatomical connectivity) and the visualization of abstract data
(InfoVis, for functional connectivity data). The goal is to be able to start
the interactive investigation with either type of data, being able to
interactively and freely switch between the different representations as it
is needed for the data exploration. The ultimate vision is two-fold: The
first and foremost aspect is to get to a more in-depth understanding on how
to support interactive data exploration using various new and
state-of-the-art visualization techniques in the neurosciences. The second
aspect is that to more generally push the boundaries of multimodal
visualization to be able to generalize the findings of this research to
other fields that work on a daily basis with data that has both spatial and
abstract characteristics.
For this purpose the project extends past results in the visualization of
dense line data as well as the visualization of weighted graphs. For the
first aspect of anatomical connectivity, the project will use methods from
illustrative visualization to deal with the dense fibertract datasets that
are generated from diffusion-weighted MRI. This aspect of the visualization
will provide an important visual reference and landmark for the exploration
of functional connectivity, for which the project will rely on the
visualization of weighted graphs. A general challenge in this context is the
question on how to create a visualization that combines both data types,
either in separate views or in a combined view. Separate linked views are
common in abstract data visualization and we will thus explore their
application for our application. A view that integrates both could combine
fibertracts inside the brain for anatomical connectivity with a bundled view
of functional links on its outside. This approach has the potential benefit
of not requiring a mental integration of separate points of reference.
On the other hand, this approach may lead to a cluttered and overloaded
depiction. The project will therefore explore new ways of controlling the
abstraction in the data depiction to deal with this issue to be able to show
the realistic large and complex datasets. This work will thus also require
research to understand how to apply illustrative visualization to abstract
data.
Such visualizations of the fibertracts in a more or less realistic way is
convenient for the neuroscientist, but we have to consider other
complementary linked views. These views often do not match the realistic
physical appearance of fibertracts but instead focus on the task at hand the
neuroscientist wants to perform with the data. So other representations than
graphs will be explored as part of this project such as scatter plots or
space-filling or pixel-based designs enabling to provide a mapping of the
information space more efficient to solve a specific visual analytic task.
Machine learrning techniques such as generative graphs will be used to
automatically extract summaries of the anatomical and functional
connectivity data. These geometrical and topological summaries will be used
as a backbone structure for the visualization of the information space to be
used for visual analysis tasks.
Moreover, an integral aspect of our approach is to combine the visualization
techniques in an interactive exploration tool that supports analists in
adjusting their exploration strategy as needed. An integral part of the
neuroanatomists' data exploration is the comparison of different datasets,
either derived from different people or captured at different points in
time. Therefore, the comparison of different datasets and the temporal
exploration will be an essential aspect of the project. To be successful in
this project, the PhD student will work closely with domain experts in the
neurosciences from the Université Pierre et Marie Curie, both to develop the
integrated interactive visualization techniques using a participatory design
approach as well as to evaluate the new techniques in controlled
experiements.
By closely working with the domain experts, the PhD student will work toward
an interactive tool for neuroanatomical data exploration that integrates the
new visualization techniques. The goal for this tool is that it can be used
in a realistic context for the everyday analysis tasks of the
neuroanatomists and that it will be provided to the public as open-source
software. Beyond this implementation, the project will result in a deeper
understanding of how to combine spatially explicit data with connected
abstract data aspects to benefit visualization in the sciences in general.
The PhD research will be conducted under the supervision of Tobias Isenberg
and within the AVIZ research team at INRIA Saclay—Île-de-France which
concentrates on the visualization of complex data. AVIZ is one of the most
respected research labs in information visualization and visual analytics
worldwide. The PhD student will closely collaborate, in particular, with
Cédric Gouy-Pailler from the Laboratoire Analyse de Données et Intelligence
des Systèmes at CEA whose expertise in machine learning will be essential
for the work. In addition, we will work with domain experts in the
neurosciences from the Université Pierre et Marie Curie.
Required applicants skills:
* highly motivated student
* degree (M.Sc., M. Eng, or equivalent) in computer science or closely
related fields
* education background in one or more of the following fields:
visualization, human-computer interaction, computer graphics, and machine
learning
* interest in applications in neuroimaging or in knowledge discovery
* previous experience in these fields (in particular, neuroimaging) would be
highly beneficial
* experience in modern computer graphics (GPU) programming
* fluent in written and spoken English (French language skills are not
required but would be beneficial for living in France and interacting with
people outside of the lab)
* previous experience in research and publication of research results
beneficial
Application package:
* detailed CV
* motivation letter
* summary of the master thesis
* transcript of the grades
* contact details for two academic references
* prepare all application documents electronically and in English
* application deadline: applications are reviewed as they are received;
however, for full consideration please submit your application by September
15
Contact:
Dr. Tobias Isenberg
(http://tobias.isenberg.cc/)
Group:
AVIZ team, INRIA Saclay (20km from Paris)
(http://www.aviz.fr/)
