Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion
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Predicting activation.pdf
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Author(s)
Langs, Georg
Golland, Polina
Ghosh, Satrajit S
Date Issued
November 2015
Journal
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
Publisher
Springer
Citation
Langs, Georg, et al. “Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion.” Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015, edited by Nassir Navab et al., vol. 9350, Springer International Publishing, 2015, pp. 313–20.
Version
Author's final manuscript
Abstract
The alignment of brain imaging data for functional neuroimaging studies is challenging due to the discrepancy between correspondence of morphology, and equivalence of functional role. In this paper we map functional activation areas across individuals by a multi-atlas label fusion algorithm in a functional space. We learn the manifold of resting-state fMRI signals in each individual, and perform manifold alignment in an embedding space. We then transfer activation predictions from a source population to a target subject via multi-atlas label fusion. The cost function is derived from the aligned manifolds, so that the resulting correspondences are derived based on the similarity of intrinsic connectivity architecture. Experiments show that the resulting label fusion predicts activation evoked by various experiment conditions with higher accuracy than relying on morphological alignment. Interestingly, the distribution of this gain is distributed heterogeneously across the cortex, and across tasks. This offers insights into the relationship between intrinsic connectivity, morphology and task activation. Practically, the mechanism can serve as prior, and provides an avenue to infer task-related activation in individuals for whom only resting data is available. Keywords: Functional Connectivity, Cortical Surface, Task Activation, Target Subject, Intrinsic Connectivity
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
McGovern Institute for Brain Research at MIT
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-319-24571-3_38
