Parallel group independent component analysis for massive fMRI data sets
Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have...
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| Published in | PloS one Vol. 12; no. 3; p. e0173496 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
09.03.2017
Public Library of Science (PLoS) |
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| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0173496 |
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| Abstract | Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have become publicly available that consist of rs-fMRI scans from thousands of subjects. As a result, efficient ICA algorithms that scale well to the increased number of subjects are required. To address this problem, we propose a two-stage likelihood-based algorithm for performing group ICA, which we denote Parallel Group Independent Component Analysis (PGICA). By utilizing the sequential nature of the algorithm and parallel computing techniques, we are able to efficiently analyze data sets from large numbers of subjects. We illustrate the efficacy of PGICA, which has been implemented in R and is freely available through the Comprehensive R Archive Network, through simulation studies and application to rs-fMRI data from two large multi-subject data sets, consisting of 301 and 779 subjects respectively. |
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| AbstractList | Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have become publicly available that consist of rs-fMRI scans from thousands of subjects. As a result, efficient ICA algorithms that scale well to the increased number of subjects are required. To address this problem, we propose a two-stage likelihood-based algorithm for performing group ICA, which we denote Parallel Group Independent Component Analysis (PGICA). By utilizing the sequential nature of the algorithm and parallel computing techniques, we are able to efficiently analyze data sets from large numbers of subjects. We illustrate the efficacy of PGICA, which has been implemented in R and is freely available through the Comprehensive R Archive Network, through simulation studies and application to rs-fMRI data from two large multi-subject data sets, consisting of 301 and 779 subjects respectively. Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have become publicly available that consist of rs-fMRI scans from thousands of subjects. As a result, efficient ICA algorithms that scale well to the increased number of subjects are required. To address this problem, we propose a two-stage likelihood-based algorithm for performing group ICA, which we denote Parallel Group Independent Component Analysis (PGICA). By utilizing the sequential nature of the algorithm and parallel computing techniques, we are able to efficiently analyze data sets from large numbers of subjects. We illustrate the efficacy of PGICA, which has been implemented in R and is freely available through the Comprehensive R Archive Network, through simulation studies and application to rs-fMRI data from two large multi-subject data sets, consisting of 301 and 779 subjects respectively.Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have become publicly available that consist of rs-fMRI scans from thousands of subjects. As a result, efficient ICA algorithms that scale well to the increased number of subjects are required. To address this problem, we propose a two-stage likelihood-based algorithm for performing group ICA, which we denote Parallel Group Independent Component Analysis (PGICA). By utilizing the sequential nature of the algorithm and parallel computing techniques, we are able to efficiently analyze data sets from large numbers of subjects. We illustrate the efficacy of PGICA, which has been implemented in R and is freely available through the Comprehensive R Archive Network, through simulation studies and application to rs-fMRI data from two large multi-subject data sets, consisting of 301 and 779 subjects respectively. |
| Audience | Academic |
| Author | Lindquist, Martin A. Nebel, Mary Beth Huang, Lei Mostofsky, Stewart H. Caffo, Brian S. Pekar, James J. Eloyan, Ani Chen, Shaojie Qiu, Huitong |
| AuthorAffiliation | 5 Department of Neurology, Johns Hopkins University, Baltimore, United States of America 2 School of Medicine, Johns Hopkins University, Baltimore, United States of America 4 Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, United States of America 1 Department of Biostatistics, Johns Hopkins University, Baltimore, United States of America University of Texas at Austin, UNITED STATES 3 Kennedy Krieger Institute, Baltimore, United States of America 6 Department of Biostatistics, Brown University, Providence, Rhode Island, United States of America |
| AuthorAffiliation_xml | – name: 1 Department of Biostatistics, Johns Hopkins University, Baltimore, United States of America – name: 4 Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, United States of America – name: 3 Kennedy Krieger Institute, Baltimore, United States of America – name: 5 Department of Neurology, Johns Hopkins University, Baltimore, United States of America – name: 2 School of Medicine, Johns Hopkins University, Baltimore, United States of America – name: 6 Department of Biostatistics, Brown University, Providence, Rhode Island, United States of America – name: University of Texas at Austin, UNITED STATES |
| Author_xml | – sequence: 1 givenname: Shaojie orcidid: 0000-0002-3543-8964 surname: Chen fullname: Chen, Shaojie – sequence: 2 givenname: Lei surname: Huang fullname: Huang, Lei – sequence: 3 givenname: Huitong surname: Qiu fullname: Qiu, Huitong – sequence: 4 givenname: Mary Beth surname: Nebel fullname: Nebel, Mary Beth – sequence: 5 givenname: Stewart H. surname: Mostofsky fullname: Mostofsky, Stewart H. – sequence: 6 givenname: James J. surname: Pekar fullname: Pekar, James J. – sequence: 7 givenname: Martin A. surname: Lindquist fullname: Lindquist, Martin A. – sequence: 8 givenname: Ani surname: Eloyan fullname: Eloyan, Ani – sequence: 9 givenname: Brian S. surname: Caffo fullname: Caffo, Brian S. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28278208$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_envint_2023_108344 crossref_primary_10_1016_j_jneumeth_2018_02_013 crossref_primary_10_1007_s12561_017_9195_y crossref_primary_10_1186_s41044_018_0033_0 crossref_primary_10_1515_revneuro_2019_0108 |
| Cites_doi | 10.1162/neco.1995.7.6.1129 10.1002/hbm.21170 10.1016/j.neuroimage.2008.10.057 10.1145/1465482.1465560 10.1002/hbm.1048 10.1016/j.neuroimage.2015.05.047 10.1002/hbm.23086 10.1073/pnas.0911855107 10.1038/mp.2013.78 10.3389/fnsys.2014.00080 10.3389/fnsys.2014.00106 10.1016/j.neuroimage.2008.05.008 10.1080/10485252.2010.532554 10.1016/0165-1684(91)90079-X 10.1016/j.neuroimage.2004.10.043 10.1016/j.neuroimage.2014.07.051 10.1016/j.neuroimage.2004.07.051 10.1016/j.neuroimage.2009.12.008 10.1162/neco.1997.9.7.1483 10.1016/j.neuroimage.2012.11.008 10.1093/biostatistics/kxs055 10.1109/TC.1972.5009071 10.1002/mrm.22818 10.3389/fnsys.2011.00002 10.1137/100804139 |
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| Copyright | COPYRIGHT 2017 Public Library of Science 2017 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2017 Chen et al 2017 Chen et al |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Conceptualization: BC AE ML JP. Data curation: MB SM JP. Formal analysis: SC LH HQ AE. Funding acquisition: BC. Investigation: MB SM JP AE. Methodology: SC LH HQ MB BC AE. Project administration: BC AE ML. Resources: SM MB JP ML. Software: SC LH HQ AE BC. Supervision: BC AE ML JP SM MB. Validation: MB AE SM JP BC. Writing – original draft: SC AE BC LH HQ MB SM JP ML. Writing – review & editing: SC AE BC LH HQ MB SM JP ML. Competing Interests: The authors have declared that no competing interests exist. |
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| SubjectTerms | Adolescent Adult Algorithms Autism Autism Spectrum Disorder - diagnostic imaging Bioengineering Biology and Life Sciences Brain Brain Mapping Child Computer and Information Sciences Computer simulation Data processing Datasets Functional magnetic resonance imaging Health aspects Humans Image Processing, Computer-Assisted - methods Independent component analysis Magnetic Resonance Imaging Medical imaging Medicine and Health Sciences Methods Neuroimaging Neurology Neurosciences Physical Sciences Principal components analysis Research and Analysis Methods Young Adult |
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| Title | Parallel group independent component analysis for massive fMRI data sets |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/28278208 https://www.proquest.com/docview/1875828216 https://www.proquest.com/docview/1876499348 https://www.proquest.com/docview/1881758897 https://pubmed.ncbi.nlm.nih.gov/PMC5344430 https://doaj.org/article/4b9775c3f9db43ddaa5ef0a106c50bf2 http://dx.doi.org/10.1371/journal.pone.0173496 |
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