{"id":"https://openalex.org/W2155515508","doi":"https://doi.org/10.1109/isbi.2010.5490400","title":"Canonical correlation analysis applied to functional connectivity in MEG","display_name":"Canonical correlation analysis applied to functional connectivity in MEG","publication_year":2010,"publication_date":"2010-01-01","ids":{"openalex":"https://openalex.org/W2155515508","doi":"https://doi.org/10.1109/isbi.2010.5490400","mag":"2155515508"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2010.5490400","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2010.5490400","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016034709","display_name":"Juan L. P. Soto","orcid":"https://orcid.org/0000-0002-1348-3633"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"J.L.P. Soto","raw_affiliation_strings":["Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030177736","display_name":"Dimitrios Pantazis","orcid":"https://orcid.org/0000-0001-8246-8878"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"D. Pantazis","raw_affiliation_strings":["Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051870567","display_name":"Karim Jerbi","orcid":"https://orcid.org/0000-0002-3790-9651"},"institutions":[{"id":"https://openalex.org/I100532134","display_name":"Universit\u00e9 Claude Bernard Lyon 1","ror":"https://ror.org/029brtt94","country_code":"FR","type":"education","lineage":["https://openalex.org/I100532134","https://openalex.org/I203339264"]},{"id":"https://openalex.org/I4210095118","display_name":"Centre de Recherche en Neurosciences de Lyon","ror":"https://ror.org/00pdd0432","country_code":"FR","type":"facility","lineage":["https://openalex.org/I100532134","https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I154526488","https://openalex.org/I203339264","https://openalex.org/I203339264","https://openalex.org/I4210095118","https://openalex.org/I4210096427","https://openalex.org/I4405259976"]},{"id":"https://openalex.org/I154526488","display_name":"Inserm","ror":"https://ror.org/02vjkv261","country_code":"FR","type":"government","lineage":["https://openalex.org/I154526488"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"K. Jerbi","raw_affiliation_strings":["INSERMU82, 1, Brain Dynamics and Cognition Laboratory, University of Lyon I, Lyon, France"],"affiliations":[{"raw_affiliation_string":"INSERMU82, 1, Brain Dynamics and Cognition Laboratory, University of Lyon I, Lyon, France","institution_ids":["https://openalex.org/I154526488","https://openalex.org/I100532134","https://openalex.org/I4210095118"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039434268","display_name":"Sylvain Baillet","orcid":"https://orcid.org/0000-0002-6762-5713"},"institutions":[{"id":"https://openalex.org/I204308271","display_name":"Medical College of Wisconsin","ror":"https://ror.org/00qqv6244","country_code":"US","type":"education","lineage":["https://openalex.org/I204308271"]},{"id":"https://openalex.org/I1310546135","display_name":"Froedtert Hospital","ror":"https://ror.org/01nhrc260","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1310546135"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"S. Baillet","raw_affiliation_strings":["Medical College of Wisconsin, Froedtert Hospital, Milwaukee, WI, USA"],"affiliations":[{"raw_affiliation_string":"Medical College of Wisconsin, Froedtert Hospital, Milwaukee, WI, USA","institution_ids":["https://openalex.org/I1310546135","https://openalex.org/I204308271"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054387045","display_name":"Richard M. Leahy","orcid":"https://orcid.org/0000-0002-7278-5471"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"R.M. Leahy","raw_affiliation_strings":["Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5016034709"],"corresponding_institution_ids":["https://openalex.org/I1174212"],"apc_list":null,"apc_paid":null,"fwci":1.1048,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.78043734,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"113","last_page":"116"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9851999878883362,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/canonical-correlation","display_name":"Canonical correlation","score":0.9068131446838379},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.7683017253875732},{"id":"https://openalex.org/keywords/magnetoencephalography","display_name":"Magnetoencephalography","score":0.6502716541290283},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5522826910018921},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5321534872055054},{"id":"https://openalex.org/keywords/distance-correlation","display_name":"Distance correlation","score":0.48130738735198975},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4746675491333008},{"id":"https://openalex.org/keywords/mixing","display_name":"Mixing (physics)","score":0.46174174547195435},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.44330716133117676},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4317959249019623},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32472625374794006},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.2364571988582611},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.22755801677703857},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.21098974347114563},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.10618835687637329},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.08299419283866882}],"concepts":[{"id":"https://openalex.org/C153874254","wikidata":"https://www.wikidata.org/wiki/Q115542","display_name":"Canonical correlation","level":2,"score":0.9068131446838379},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.7683017253875732},{"id":"https://openalex.org/C556910895","wikidata":"https://www.wikidata.org/wiki/Q384188","display_name":"Magnetoencephalography","level":3,"score":0.6502716541290283},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5522826910018921},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5321534872055054},{"id":"https://openalex.org/C121694360","wikidata":"https://www.wikidata.org/wiki/Q5282862","display_name":"Distance correlation","level":3,"score":0.48130738735198975},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4746675491333008},{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.46174174547195435},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.44330716133117676},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4317959249019623},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32472625374794006},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.2364571988582611},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.22755801677703857},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.21098974347114563},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.10618835687637329},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.08299419283866882},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi.2010.5490400","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2010.5490400","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2035883146","https://openalex.org/W2061182809","https://openalex.org/W2088766828","https://openalex.org/W2096417444","https://openalex.org/W2100879297","https://openalex.org/W2163274894","https://openalex.org/W4235611253"],"related_works":["https://openalex.org/W2367413540","https://openalex.org/W1968846550","https://openalex.org/W2157963363","https://openalex.org/W1991315556","https://openalex.org/W2110970463","https://openalex.org/W4387164999","https://openalex.org/W2070623039","https://openalex.org/W2006749424","https://openalex.org/W3131670725","https://openalex.org/W2334424990"],"abstract_inverted_index":{"We":[0,22,91,110],"present":[1],"a":[2,24,94],"multivariate":[3],"method":[4,113],"based":[5],"on":[6,31],"canonical":[7,59,80],"correlation":[8,81,106],"analysis":[9],"for":[10,85,99],"the":[11,17,28,32,37,58,62,68,76,105,139],"study":[12],"of":[13,27,51,70],"functional":[14,132],"connectivity":[15],"in":[16],"brain":[18,29],"with":[19,65],"MEG":[20,121],"data.":[21],"obtain":[23],"time-frequency":[25],"representation":[26],"activity":[30],"cortical":[33],"surface,":[34],"and":[35,61,116,124],"use":[36],"signal":[38],"power":[39],"at":[40],"specific":[41],"frequency":[42,73,140],"bands":[43,141],"as":[44,136,138],"inputs":[45],"to":[46,75,97,114,130,144],"our":[47,112],"model.":[48],"Our":[49],"measure":[50],"interaction":[52],"between":[53],"two":[54],"spatial":[55],"locations":[56],"is":[57,128],"correlation,":[60],"vectors":[63,107],"associated":[64],"it":[66,127],"indicate":[67],"contribution":[69],"each":[71],"individual":[72],"band":[74],"interaction.":[77],"The":[78],"resulting":[79],"maps":[82],"are":[83,108],"thresholded":[84],"significance":[86],"using":[87],"false":[88],"discovery":[89],"rate.":[90],"further":[92],"provide":[93],"novel":[95],"way":[96],"control":[98],"linear":[100],"mixing":[101],"by":[102],"testing":[103],"whether":[104],"collinear.":[109],"apply":[111],"simulations":[115],"experimental":[117],"data":[118],"from":[119],"an":[120],"visuomotor":[122],"study,":[123],"demonstrate":[125],"that":[126,142],"able":[129],"detect":[131],"interactions":[133],"across":[134],"space":[135],"well":[137],"contribute":[143],"these":[145],"interactions.":[146]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
