Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion
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 fun...
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| Format: | Article |
| Language: | en_US |
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Springer
2018
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| Online Access: | http://hdl.handle.net/1721.1/115187 https://orcid.org/0000-0003-2516-731X https://orcid.org/0000-0002-5312-6729 |
| _version_ | 1811097575250460672 |
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| author | Langs, Georg Golland, Polina Ghosh, Satrajit S |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Langs, Georg Golland, Polina Ghosh, Satrajit S |
| author_sort | Langs, Georg |
| collection | MIT |
| description | 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 |
| first_indexed | 2024-09-23T17:01:47Z |
| format | Article |
| id | mit-1721.1/115187 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T17:01:47Z |
| publishDate | 2018 |
| publisher | Springer |
| record_format | dspace |
| spelling | mit-1721.1/1151872022-09-29T23:09:48Z Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion Langs, Georg Golland, Polina Ghosh, Satrajit S 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 Langs, Georg Golland, Polina Ghosh, Satrajit S 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 Congressionally Directed Medical Research Programs (U.S.) (Grant PT100120) Eunice Kennedy Shriver National Institute of Child Health and Human Development (U.S.) (R01HD067312) Neuroimaging Analysis Center (U.S.) (P41EB015902) Oesterreichische Nationalbank (14812) Oesterreichische Nationalbank (15929) Seventh Framework Programme (European Commission) (FP7 2012-PIEF-GA-33003) 2018-05-02T18:37:54Z 2018-05-02T18:37:54Z 2015-11 Article http://purl.org/eprint/type/ConferencePaper 978-3-319-24570-6 978-3-319-24571-3 0302-9743 1611-3349 http://hdl.handle.net/1721.1/115187 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. https://orcid.org/0000-0003-2516-731X https://orcid.org/0000-0002-5312-6729 en_US http://dx.doi.org/10.1007/978-3-319-24571-3_38 Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Springer PMC |
| spellingShingle | Langs, Georg Golland, Polina Ghosh, Satrajit S Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion |
| title | Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion |
| title_full | Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion |
| title_fullStr | Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion |
| title_full_unstemmed | Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion |
| title_short | Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion |
| title_sort | predicting activation across individuals with resting state functional connectivity based multi atlas label fusion |
| url | http://hdl.handle.net/1721.1/115187 https://orcid.org/0000-0003-2516-731X https://orcid.org/0000-0002-5312-6729 |
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