Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6025798 | NeuroImage | 2014 | 14 Pages |
Abstract
In this paper we construct an atlas that summarizes functional connectivity characteristics of a cognitive process from a population of individuals. The atlas encodes functional connectivity structure in a low-dimensional embedding space that is derived from a diffusion process on a graph that represents correlations of fMRI time courses. The functional atlas is decoupled from the anatomical space, and thus can represent functional networks with variable spatial distribution in a population. In practice the atlas is represented by a common prior distribution for the embedded fMRI signals of all subjects. We derive an algorithm for fitting this generative model to the observed data in a population. Our results in a language fMRI study demonstrate that the method identifies coherent and functionally equivalent regions across subjects. The method also successfully maps functional networks from a healthy population used as a training set to individuals whose language networks are affected by tumors.
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Authors
Georg Langs, Andrew Sweet, Danial Lashkari, Yanmei Tie, Laura Rigolo, Alexandra J. Golby, Polina Golland,