Decoupling Function and Anatomy in Atlases of Functional Connectivity Patterns: Language Mapping in Tumor Patients

Citation:

Georg Langs, Andrew Sweet, Danial Lashkari, Yanmei Tie, Laura Rigolo, Alexandra J Golby, and Polina Golland. 2014. “Decoupling Function and Anatomy in Atlases of Functional Connectivity Patterns: Language Mapping in Tumor Patients.” Neuroimage, 103, Pp. 462-75.

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.
Last updated on 10/19/2017