Exploring functional connectivity in fMRI via clustering
In this paper we investigate the use of data driven clustering methods for functional connectivity analysis in fMRI. In particular, we consider the k-means and spectral clustering algorithms as alternatives to the commonly used seed-based analysis. To enable clustering of the entire brain volume, we...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2011
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| Online Access: | http://hdl.handle.net/1721.1/62022 https://orcid.org/0000-0003-2516-731X |
| _version_ | 1826216806628458496 |
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| author | Venkataraman, Archana Van Dijk, Koene R. A. Buckner, Randy L. Golland, Polina |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Venkataraman, Archana Van Dijk, Koene R. A. Buckner, Randy L. Golland, Polina |
| author_sort | Venkataraman, Archana |
| collection | MIT |
| description | In this paper we investigate the use of data driven clustering methods for functional connectivity analysis in fMRI. In particular, we consider the k-means and spectral clustering algorithms as alternatives to the commonly used seed-based analysis. To enable clustering of the entire brain volume, we use the Nystrom Method to approximate the necessary spectral decompositions. We apply k-means, spectral clustering and seed-based analysis to resting-state fMRI data collected from 45 healthy young adults. Without placing any a priori constraints, both clustering methods yield partitions that are associated with brain systems previously identified via seed-based analysis. Our empirical results suggest that clustering provides a valuable tool for functional connectivity analysis. |
| first_indexed | 2024-09-23T16:53:44Z |
| format | Article |
| id | mit-1721.1/62022 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:53:44Z |
| publishDate | 2011 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/620222022-10-03T08:59:12Z Exploring functional connectivity in fMRI via clustering Venkataraman, Archana Van Dijk, Koene R. A. Buckner, Randy L. Golland, Polina Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Golland, Polina Venkataraman, Archana Golland, Polina In this paper we investigate the use of data driven clustering methods for functional connectivity analysis in fMRI. In particular, we consider the k-means and spectral clustering algorithms as alternatives to the commonly used seed-based analysis. To enable clustering of the entire brain volume, we use the Nystrom Method to approximate the necessary spectral decompositions. We apply k-means, spectral clustering and seed-based analysis to resting-state fMRI data collected from 45 healthy young adults. Without placing any a priori constraints, both clustering methods yield partitions that are associated with brain systems previously identified via seed-based analysis. Our empirical results suggest that clustering provides a valuable tool for functional connectivity analysis. National Alliance for Medical Image Computing (U.S.) (NIH NIBIB NAMIC U54-EB005149) Neuroimaging Analysis Center (U.S.) (NIH NCRR NAC P41-RR13218) National Science Foundation (U.S.) (CAREER grant 0642971) Howard Hughes Medical Institute National Defense Science and Engineering Graduate Fellowship 2011-04-01T21:16:44Z 2011-04-01T21:16:44Z 2009-04 Article http://purl.org/eprint/type/ConferencePaper 978-1-4244-2353-8 1520-6149 INSPEC Accession Number: 10700583 http://hdl.handle.net/1721.1/62022 Venkataraman, A. et al. “Exploring Functional Connectivity in fMRI via Clustering.” Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference On. 2009. 441-444. Copyright © 2009, IEEE https://orcid.org/0000-0003-2516-731X en_US http://dx.doi.org/10.1109/ICASSP.2009.4959615 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers MIT web domain |
| spellingShingle | Venkataraman, Archana Van Dijk, Koene R. A. Buckner, Randy L. Golland, Polina Exploring functional connectivity in fMRI via clustering |
| title | Exploring functional connectivity in fMRI via clustering |
| title_full | Exploring functional connectivity in fMRI via clustering |
| title_fullStr | Exploring functional connectivity in fMRI via clustering |
| title_full_unstemmed | Exploring functional connectivity in fMRI via clustering |
| title_short | Exploring functional connectivity in fMRI via clustering |
| title_sort | exploring functional connectivity in fmri via clustering |
| url | http://hdl.handle.net/1721.1/62022 https://orcid.org/0000-0003-2516-731X |
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