Chapter title |
Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion
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---|---|
Chapter number | 38 |
Book title |
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, October 2015
|
DOI | 10.1007/978-3-319-24571-3_38 |
Pubmed ID | |
Book ISBNs |
978-3-31-924570-6, 978-3-31-924571-3
|
Authors |
Georg Langs, Polina Golland, Satrajit S. Ghosh |
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. |
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Geographical breakdown
Country | Count | As % |
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Spain | 1 | 2% |
Unknown | 43 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 10 | 23% |
Researcher | 6 | 14% |
Student > Postgraduate | 3 | 7% |
Student > Master | 3 | 7% |
Student > Doctoral Student | 2 | 5% |
Other | 4 | 9% |
Unknown | 16 | 36% |
Readers by discipline | Count | As % |
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Neuroscience | 12 | 27% |
Computer Science | 5 | 11% |
Agricultural and Biological Sciences | 3 | 7% |
Psychology | 3 | 7% |
Nursing and Health Professions | 1 | 2% |
Other | 0 | 0% |
Unknown | 20 | 45% |