Learning and comparing multi-subject models of brain functional connectivity
Speaker: Gael Varoquaux , NeuroSpin, CEA, France
Date: September 8 2011
Time: 4:00PM to 5:00PM
Location: 32-D507
Host: Polina Golland, CSAIL
Contact: Polina Golland, 6172538005, polina@csail.mit.edu
High-level brain function arises through functional interactions. These
can be mapped via co-fluctuations in activity observed in functional
imaging.
First, I will show how spatial maps characteristic of on-going activity in
a population of subjects can be learned using multi-subject decomposition
models extending the popular Independent Component Analysis. These
methods single out spatial atoms of brain activity: functional networks
or brain regions. With a probabilistic model of inter-subject
variability, they open the door to building data-driven atlases of
on-going activity.
Subsequently, I will discuss graphical modeling of the interactions
between brain regions. To learn highly-resolved large scale individual
graphical models models, we use sparsity-inducing penalizations
introducing a population prior that mitigates the data scarcity at the
subject-level. The corresponding graphs capture better the community
structure of brain activity than single-subject models or group averages.
Finally, I will address the detection of connectivity differences between
subjects. Explicit group variability models of the covariance structure
can be used to build optimal edge-level test statistics. On stroke
patients resting-state data, these models detect patient-specific
functional connectivity perturbations.
See other events that are part of Biomedical Imaging and Analysis 2011/2012
See other events happening in September 2011
