{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T11:58:44Z","timestamp":1767182324009,"version":"build-2238731810"},"reference-count":20,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2013,8,29]],"date-time":"2013-08-29T00:00:00Z","timestamp":1377734400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"DOI":"10.1371\/journal.pone.0073309","type":"journal-article","created":{"date-parts":[[2013,8,29]],"date-time":"2013-08-29T17:11:21Z","timestamp":1377796281000},"page":"e73309","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":58,"title":["Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence"],"prefix":"10.1371","volume":"8","author":[{"given":"Vince D.","family":"Calhoun","sequence":"first","affiliation":[]},{"given":"Vamsi K.","family":"Potluru","sequence":"additional","affiliation":[]},{"given":"Ronald","family":"Phlypo","sequence":"additional","affiliation":[]},{"given":"Rogers F.","family":"Silva","sequence":"additional","affiliation":[]},{"given":"Barak A.","family":"Pearlmutter","sequence":"additional","affiliation":[]},{"given":"Arvind","family":"Caprihan","sequence":"additional","affiliation":[]},{"given":"Sergey M.","family":"Plis","sequence":"additional","affiliation":[]},{"given":"T\u00fclay","family":"Adal\u0131","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2013,8,29]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1002\/(SICI)1097-0193(1998)6:3<160::AID-HBM5>3.0.CO;2-1","article-title":"Analysis of fMRI Data by Blind Separation Into Independent Spatial Components","volume":"6","author":"MJ McKeown","year":"1998","journal-title":"HumBrain Map"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1002\/hbm.1048","article-title":"A Method for Making Group Inferences from Functional MRI Data Using Independent Component Analysis","volume":"14","author":"VD Calhoun","year":"2001","journal-title":"HumBrain Map"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1162\/neco.1995.7.6.1129","article-title":"An information maximisation approach to blind separation and blind deconvolution","volume":"7","author":"AJ Bell","year":"1995","journal-title":"Neural Comput"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1162\/089976699300016863","article-title":"High-order contrasts for independent component analysis","volume":"11","author":"JF Cardoso","year":"1999","journal-title":"Neural Comput"},{"key":"ref5","first-page":"19","article-title":"Online learning for matrix factorization and sparse coding","volume":"11","author":"J Mairal","year":"2010","journal-title":"Journal of Machine Learning Research"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1162\/089976601300014385","article-title":"Blind source separation by sparse decomposition in a signal dictionary","volume":"13","author":"M Zibulevsky","year":"2001","journal-title":"Neural Comput"},{"key":"ref7","first-page":"1457","article-title":"Non-negative matrix factorization with sparseness constraints","volume":"5","author":"P Hoyer","year":"2004","journal-title":"Journal of Machine Learning Research"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"10415","DOI":"10.1073\/pnas.0903525106","article-title":"Independent component analysis for brain fMRI does not select for independence","volume":"106","author":"I Daubechies","year":"2009","journal-title":"Proc Natl Acad Sci U S A"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1162\/neco.1997.9.7.1483","article-title":"A fast fixed-point algorithm for independent component analysis","volume":"9","author":"A Hyvarinen","year":"1997","journal-title":"Neural Comput"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1162\/089976699300016458","article-title":"Independent factor analysis","volume":"11","author":"H Attias","year":"1999","journal-title":"Neural Comput"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Li H, Adal\u0131 T, Correa N, Rodriguez P, Calhoun VD (2010) Flexible Complex ICA of fMRI Data; Dallas, TX.","DOI":"10.1109\/ICASSP.2010.5495005"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1109\/TCSI.2010.2046207","article-title":"Complex independent component analysis by entropy bound minimization","volume":"57","author":"X-L Li","year":"2010","journal-title":"IEEE Trans Circuits and Systems I"},{"key":"ref13","unstructured":"Macchi O, Moreau E (1995) Self-Adaptive Source Separation by Direct or Recursive Networks; Limasol, Cyprus. 122\u2013129."},{"key":"ref14","unstructured":"Calhoun VD, Adal\u0131 T (in press) Multi-subject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery. IEEE Reviews in Biomedical Engineering."},{"key":"ref15","first-page":"684","article-title":"Performance of Blind Source Separation Algorithms for fMRI Analysis","volume":"25","author":"N Correa","year":"2007","journal-title":"MagResImag"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1002\/hbm.20100","article-title":"Source-Density Driven Independent Component Analysis Approach for fMRI Data","volume":"25","author":"B Hong","year":"2005","journal-title":"HumBrain Map"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"2794","DOI":"10.1109\/TBME.2011.2159841","article-title":"Application of Independent Component Analysis with Adaptive Density Model to Complex-valued fMRI Data","volume":"58","author":"H Li","year":"2011","journal-title":"IEEE Trans Biomed Eng"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"Du W, Li H, Li X-L, Calhoun VD, Adal\u0131 T (2011) ICA of fMRI data: Performance of Three ICA Algorithms and the Importance of Taking Correlation Information into Account; Chicago, IL.","DOI":"10.1109\/ISBI.2011.5872702"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1162\/089976699300016719","article-title":"Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources","volume":"11","author":"TW Lee","year":"1999","journal-title":"Neural Comput"},{"key":"ref20","first-page":"620","article-title":"Independent component analysis of functional MRI: what is signal and what is noise?","volume":"13","author":"MJ McKeown","year":"2003","journal-title":"CurrOpinNeurobiol"}],"updated-by":[{"DOI":"10.1371\/annotation\/52c7b854-2d52-4b49-9f9f-6560830f9428","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2013,10,24]],"date-time":"2013-10-24T00:00:00Z","timestamp":1382572800000}}],"container-title":["PLoS ONE"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/dx.plos.org\/10.1371\/journal.pone.0073309","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,9]],"date-time":"2020-05-09T13:40:36Z","timestamp":1589031636000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0073309"}},"subtitle":[],"editor":[{"given":"Dante R.","family":"Chialvo","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2013,8,29]]},"references-count":20,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2013,8,29]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0073309","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,8,29]]}}}