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Abstract
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Mapping brain functional connectivity from functional Magnetic Resonance Imaging (MRI) data
has become a very active field of research. However, analysis tools are limited and many impor-
tant tasks, such as the empirical definition of brain networks, remain difficult due to the lack of a
good framework for the statistical modeling of these networks. We propose to develop population
models of anatomical and functional connectivity data to improve the alignment of subjects brain
structures of interest while inferring an average template of these structures. Based on this es-
sential contribution, we will design new statistical inference procedures to compare the functional
connections between conditions or populations and improve the sensitivity of connectivity analy-
sis performed on noisy data. Finally, we will test and validate the methods on multiple datasets
and distribute them to the brain imaging community.
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Work program
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The project consists in blending functional information into brain image registration algorithms. We will inject functional information by coupling a deformation framework with a set of contrasts, that comprise spatial maps obtained from multi-subject dictionary learning procedures 4, in order to jointly estimate functional regions and coregister individual data. Regarding the registration problem, we will consider different standard alternative models, the cost and merit of which (in terms of cost and accuracy) will be assessed carefully:
• The diffeomorphic log-daemons framework, which is efficient, has proven to be effective in many contexts and is well-mastered by our lab (PhD thesis of V. Siless).
• Several avatars of the LDDMM framework 5, that is considered as the state-of-the art approach
• Discrete optimization approaches 3 for multi-modal cross-subject registrations, that has particular strengths, such as a lesser sensitivity to initialization.
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