Learning an atlas of a cognitive process in its functional geometry
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Golland_Learning an atlas.pdf
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Author(s)
Langs, Georg
Lashkari, Danial
Sweet, Andrew Douglas
Tie, Yanmei
Rigolo, Laura
Golby, Alexandra J.
Golland, Polina
Date Issued
June 2011
Journal
Information Processing in Medical Imaging
Publisher
Springer-Verlag
Citation
Langs, Georg et al. “Learning an Atlas of a Cognitive Process in Its Functional Geometry.” Information Processing in Medical Imaging. Ed. Gábor Székely & Horst K. Hahn. LNCS, Vol. 6801. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. 135-146.
Version
Author's final manuscript
Abstract
In this paper we construct an atlas that captures functional characteristics of a cognitive process from a population of individuals. The functional connectivity is encoded in a low-dimensional embedding space derived from a diffusion process on a graph that represents correlations of fMRI time courses. The atlas is represented by a common prior distribution for the embedded fMRI signals of all subjects. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution. 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.
Description
Proceedings of the 22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1007/978-3-642-22092-0_12
