Relating brain functional connectivity to anatomical connections: prediction and inference
Speaker: Fani Deligianni , Imperial College London, UK
Date: September 9 2011
Time: 1:30PM to 2:30PM
Location: 32-D507
Host: Polina Golland, CSAIL
Contact: Polina Golland, 6172538005, polina@csail.mit.edu
It has become evident that connectivity is a major factor influencing the brain’s computation power and
stability and it is not adequate to investigate functional specialisation without considering how different
brain areas interact. In fact, disturbances of brain connectivity have been implicated in a number of diseases
including schizophrenia ADHD, autism AD, stroke and brain trauma. This has resulted in recent interest in
network organisation and dynamics. Magnetic Resonance Imaging (MRI) can be used to derive structural
and functional brain networks from diffusion weighted MRI (DWI) and resting-state functional MRI (rsfMRI).
Several tractography techniques have been developed that exploit voxel-based directional
information to extract structural networks from DWI. On the other hand, functional networks are defined
based on the temporal correlations between spatially remote neurophysiological events. The goal of
combining these approaches is to provide a whole-brain connectivity description that reflects structure and
function. Integrating measures of structural and functional brain connectivity holds the promise of
dramatically improving our understanding of brain function and malfunction and could lead to the
development of clinically useful biomarkers.
In my talk I will present a systematic framework to learn across several subjects a mapping from brain
anatomical connectivity to functional connectivity based on inference. I will show the knowledge we
gained by a number of different approaches and how this led us to a structured-output learning task in order
to account for the strongly correlated parameters. The key advantage of our latest approach is that it
accounts for indirect connectivity and it utilises a generative model based on graphical models of
autoregressive Gaussian processes. A graphical model of the fMRI time series is an undirected graph with
nodes equal to the number of ROIs. Each pair of nodes is connected with an edge if the underline time
series are conditionally dependent, given the other time series. The problem to solve is known as
covariance selection problem, which is the problem of computing the maximum likelihood estimate of the
inverse covariance matrix of a multivariate Gaussian variable, subject to conditional independence
constrains. We used the common structure based on the structural connectivity to impose conditional
independence and thus to enhance the robustness of the estimation of the covariance matrix. This natural
parameterization of functional connectivity also enforces the positive-definiteness of the predicted
covariance and thus matches the structure of the output space. Our results show that functional connectivity
can be explained by anatomical connectivity on a rigorous statistical basis.
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