Comparison of three methods for generating group statistical inferences from independent component analysis of functional magnetic resonance imaging data
Purpose To evaluate the relative effectiveness of three previously proposed methods of performing group independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. Materials and Methods Data were generated via computer simulation. Components were added to a varying num...
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Veröffentlicht in: | Journal of magnetic resonance imaging 2004-03, Vol.19 (3), p.365-368 |
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creator | Schmithorst, Vincent J. Holland, Scott K. |
description | Purpose
To evaluate the relative effectiveness of three previously proposed methods of performing group independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data.
Materials and Methods
Data were generated via computer simulation. Components were added to a varying number of subjects between 1 and 20, and intersubject variability was simulated for both the added sources and their associated time courses. Three methods of group ICA analyses were performed: across‐subject averaging, subject‐wise concatenation, and row‐wise concatenation (e.g., across time courses).
Results
Concatenating across subjects provided the best overall performance in terms of accurate estimation of the sources and associated time courses. Averaging across subjects provided accurate estimation (R > 0.9) of the time courses when the sources were present in a sufficient fraction (about 15%) of 100 subjects. Concatenating across time courses was shown not to be a feasible method when unique sources were added to the data from each subject, simulating the effects of motion and susceptibility artifacts.
Conclusion
Subject‐wise concatenation should be used when computationally feasible. For studies involving a large number of subjects, across‐subject averaging provides an acceptable alternative and reduces the computational load. J. Magn. Reson. Imaging 2004;19:365–368. © 2004 Wiley‐Liss, Inc. |
doi_str_mv | 10.1002/jmri.20009 |
format | Article |
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To evaluate the relative effectiveness of three previously proposed methods of performing group independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data.
Materials and Methods
Data were generated via computer simulation. Components were added to a varying number of subjects between 1 and 20, and intersubject variability was simulated for both the added sources and their associated time courses. Three methods of group ICA analyses were performed: across‐subject averaging, subject‐wise concatenation, and row‐wise concatenation (e.g., across time courses).
Results
Concatenating across subjects provided the best overall performance in terms of accurate estimation of the sources and associated time courses. Averaging across subjects provided accurate estimation (R > 0.9) of the time courses when the sources were present in a sufficient fraction (about 15%) of 100 subjects. Concatenating across time courses was shown not to be a feasible method when unique sources were added to the data from each subject, simulating the effects of motion and susceptibility artifacts.
Conclusion
Subject‐wise concatenation should be used when computationally feasible. For studies involving a large number of subjects, across‐subject averaging provides an acceptable alternative and reduces the computational load. J. Magn. Reson. Imaging 2004;19:365–368. © 2004 Wiley‐Liss, Inc.</description><identifier>ISSN: 1053-1807</identifier><identifier>EISSN: 1522-2586</identifier><identifier>DOI: 10.1002/jmri.20009</identifier><identifier>PMID: 14994306</identifier><language>eng</language><publisher>Hoboken: Wiley Subscription Services, Inc., A Wiley Company</publisher><subject>algorithms ; Computer Simulation - statistics & numerical data ; Data Interpretation, Statistical ; data-driven methodologies ; functional MRI ; Humans ; image processing ; Image Processing, Computer-Assisted - methods ; information theory ; Magnetic Resonance Imaging - statistics & numerical data ; Models, Neurological ; Normal Distribution ; Time Factors</subject><ispartof>Journal of magnetic resonance imaging, 2004-03, Vol.19 (3), p.365-368</ispartof><rights>Copyright © 2004 Wiley‐Liss, Inc.</rights><rights>Copyright 2004 Wiley-Liss, Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3999-ab00958685b51d5a395b45119801627db7113b462a251b9f9d6763216d84bd093</citedby><cites>FETCH-LOGICAL-c3999-ab00958685b51d5a395b45119801627db7113b462a251b9f9d6763216d84bd093</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjmri.20009$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjmri.20009$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,1433,27924,27925,45574,45575,46409,46833</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/14994306$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Schmithorst, Vincent J.</creatorcontrib><creatorcontrib>Holland, Scott K.</creatorcontrib><title>Comparison of three methods for generating group statistical inferences from independent component analysis of functional magnetic resonance imaging data</title><title>Journal of magnetic resonance imaging</title><addtitle>J. Magn. Reson. Imaging</addtitle><description>Purpose
To evaluate the relative effectiveness of three previously proposed methods of performing group independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data.
Materials and Methods
Data were generated via computer simulation. Components were added to a varying number of subjects between 1 and 20, and intersubject variability was simulated for both the added sources and their associated time courses. Three methods of group ICA analyses were performed: across‐subject averaging, subject‐wise concatenation, and row‐wise concatenation (e.g., across time courses).
Results
Concatenating across subjects provided the best overall performance in terms of accurate estimation of the sources and associated time courses. Averaging across subjects provided accurate estimation (R > 0.9) of the time courses when the sources were present in a sufficient fraction (about 15%) of 100 subjects. Concatenating across time courses was shown not to be a feasible method when unique sources were added to the data from each subject, simulating the effects of motion and susceptibility artifacts.
Conclusion
Subject‐wise concatenation should be used when computationally feasible. For studies involving a large number of subjects, across‐subject averaging provides an acceptable alternative and reduces the computational load. J. Magn. Reson. Imaging 2004;19:365–368. © 2004 Wiley‐Liss, Inc.</description><subject>algorithms</subject><subject>Computer Simulation - statistics & numerical data</subject><subject>Data Interpretation, Statistical</subject><subject>data-driven methodologies</subject><subject>functional MRI</subject><subject>Humans</subject><subject>image processing</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>information theory</subject><subject>Magnetic Resonance Imaging - statistics & numerical data</subject><subject>Models, Neurological</subject><subject>Normal Distribution</subject><subject>Time Factors</subject><issn>1053-1807</issn><issn>1522-2586</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2004</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kc1u1DAQgC1ERUvhwgMgnzggpfVP7MRHtEApKiBVII6Wk0y2Lomd2olgH6Vvy4Tdwo2LPTP6_I3tIeQFZ2ecMXF-OyZ_Jhhj5hE54UqIQqhaP8aYKVnwmlXH5GnOtythSvWEHPMSA8n0CbnfxHFyyecYaOzpfJMA6AjzTewy7WOiWwiQ3OzDlm5TXCaaZ8zy7Fs3UB96SBBaQDbFEfMOJsAlzLRFcQxr5IIbdtnntUG_hHb2ESt0dNsA6KEJsLtDC_VYWzt1bnbPyFHvhgzPD_sp-fb-3dfNh-Lqy8Xl5s1V0UpjTOEafBU-t1aN4p1y0qimVJybmnEtqq6pOJdNqYUTijemN52utBRcd3XZdMzIU_Jq751SvFsgz3b0uYVhcAHikm3FNfq5QvD1HmxTzDlBb6eEF047y5ldB2HXQdg_g0D45cG6NCN0_9DDzyPA98BPP8DuPyr78dP15YO02J_B_4dff8-49MPqSlbKfv98YYV-q8V1ya2QvwEE1KUg</recordid><startdate>200403</startdate><enddate>200403</enddate><creator>Schmithorst, Vincent J.</creator><creator>Holland, Scott K.</creator><general>Wiley Subscription Services, Inc., A Wiley Company</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>200403</creationdate><title>Comparison of three methods for generating group statistical inferences from independent component analysis of functional magnetic resonance imaging data</title><author>Schmithorst, Vincent J. ; Holland, Scott K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3999-ab00958685b51d5a395b45119801627db7113b462a251b9f9d6763216d84bd093</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2004</creationdate><topic>algorithms</topic><topic>Computer Simulation - statistics & numerical data</topic><topic>Data Interpretation, Statistical</topic><topic>data-driven methodologies</topic><topic>functional MRI</topic><topic>Humans</topic><topic>image processing</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>information theory</topic><topic>Magnetic Resonance Imaging - statistics & numerical data</topic><topic>Models, Neurological</topic><topic>Normal Distribution</topic><topic>Time Factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Schmithorst, Vincent J.</creatorcontrib><creatorcontrib>Holland, Scott K.</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of magnetic resonance imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Schmithorst, Vincent J.</au><au>Holland, Scott K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of three methods for generating group statistical inferences from independent component analysis of functional magnetic resonance imaging data</atitle><jtitle>Journal of magnetic resonance imaging</jtitle><addtitle>J. Magn. Reson. Imaging</addtitle><date>2004-03</date><risdate>2004</risdate><volume>19</volume><issue>3</issue><spage>365</spage><epage>368</epage><pages>365-368</pages><issn>1053-1807</issn><eissn>1522-2586</eissn><abstract>Purpose
To evaluate the relative effectiveness of three previously proposed methods of performing group independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data.
Materials and Methods
Data were generated via computer simulation. Components were added to a varying number of subjects between 1 and 20, and intersubject variability was simulated for both the added sources and their associated time courses. Three methods of group ICA analyses were performed: across‐subject averaging, subject‐wise concatenation, and row‐wise concatenation (e.g., across time courses).
Results
Concatenating across subjects provided the best overall performance in terms of accurate estimation of the sources and associated time courses. Averaging across subjects provided accurate estimation (R > 0.9) of the time courses when the sources were present in a sufficient fraction (about 15%) of 100 subjects. Concatenating across time courses was shown not to be a feasible method when unique sources were added to the data from each subject, simulating the effects of motion and susceptibility artifacts.
Conclusion
Subject‐wise concatenation should be used when computationally feasible. For studies involving a large number of subjects, across‐subject averaging provides an acceptable alternative and reduces the computational load. J. Magn. Reson. Imaging 2004;19:365–368. © 2004 Wiley‐Liss, Inc.</abstract><cop>Hoboken</cop><pub>Wiley Subscription Services, Inc., A Wiley Company</pub><pmid>14994306</pmid><doi>10.1002/jmri.20009</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
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subjects | algorithms Computer Simulation - statistics & numerical data Data Interpretation, Statistical data-driven methodologies functional MRI Humans image processing Image Processing, Computer-Assisted - methods information theory Magnetic Resonance Imaging - statistics & numerical data Models, Neurological Normal Distribution Time Factors |
title | Comparison of three methods for generating group statistical inferences from independent component analysis of functional magnetic resonance imaging data |
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