{"id":"https://openalex.org/W1545779705","doi":"https://doi.org/10.1109/isbi.2015.7163965","title":"Multi-subject fMRI connectivity analysis using sparse dictionary learning and multiset canonical correlation analysis","display_name":"Multi-subject fMRI connectivity analysis using sparse dictionary learning and multiset canonical correlation analysis","publication_year":2015,"publication_date":"2015-04-01","ids":{"openalex":"https://openalex.org/W1545779705","doi":"https://doi.org/10.1109/isbi.2015.7163965","mag":"1545779705"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2015.7163965","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2015.7163965","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)","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/A5007304996","display_name":"Muhammad Usman Khalid","orcid":"https://orcid.org/0000-0003-1636-3351"},"institutions":[{"id":"https://openalex.org/I118347636","display_name":"Australian National University","ror":"https://ror.org/019wvm592","country_code":"AU","type":"education","lineage":["https://openalex.org/I118347636"]},{"id":"https://openalex.org/I42894916","display_name":"Data61","ror":"https://ror.org/03q397159","country_code":"AU","type":"other","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I42894916","https://openalex.org/I4387156119"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Muhammad Usman Khalid","raw_affiliation_strings":["ANU College of Engg. & Computer Sci, NICTA & The Australian National University, Canberra, Australia","NICTA & The Australian National University, ANU College of Engg. & Computer Sci., Canberra, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ANU College of Engg. & Computer Sci, NICTA & The Australian National University, Canberra, Australia","institution_ids":["https://openalex.org/I118347636","https://openalex.org/I42894916"]},{"raw_affiliation_string":"NICTA & The Australian National University, ANU College of Engg. & Computer Sci., Canberra, Australia","institution_ids":["https://openalex.org/I42894916","https://openalex.org/I118347636"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084681382","display_name":"Abd\u2010Krim Seghouane","orcid":"https://orcid.org/0000-0003-4619-734X"},"institutions":[{"id":"https://openalex.org/I2802046543","display_name":"Victorian Curriculum and Assessment Authority","ror":"https://ror.org/02w6dpq08","country_code":"AU","type":"government","lineage":["https://openalex.org/I2801244131","https://openalex.org/I2802046543","https://openalex.org/I4210132500"]},{"id":"https://openalex.org/I2802584246","display_name":"Melbourne School of Theology","ror":"https://ror.org/05kdkdw55","country_code":"AU","type":"education","lineage":["https://openalex.org/I2802584246"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Abd-Krim Seghouane","raw_affiliation_strings":["Dept. of Electrical & Electronic Engg, Melbourne School of Engineering, Melbourne, Australia","Melbourne School of Engineering, Dept. of Electrical & Electronic Engg., Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Electrical & Electronic Engg, Melbourne School of Engineering, Melbourne, Australia","institution_ids":["https://openalex.org/I2802584246","https://openalex.org/I2802046543"]},{"raw_affiliation_string":"Melbourne School of Engineering, Dept. of Electrical & Electronic Engg., Melbourne, Australia","institution_ids":["https://openalex.org/I2802584246"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6651,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.82480438,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"683","last_page":"686"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9998999834060669,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9998999834060669,"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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9829999804496765,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multiset","display_name":"Multiset","score":0.9289169311523438},{"id":"https://openalex.org/keywords/canonical-correlation","display_name":"Canonical correlation","score":0.8806806802749634},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6856631636619568},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.6546556353569031},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5913941860198975},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5557432174682617},{"id":"https://openalex.org/keywords/functional-magnetic-resonance-imaging","display_name":"Functional magnetic resonance imaging","score":0.4980323314666748},{"id":"https://openalex.org/keywords/independence","display_name":"Independence (probability theory)","score":0.4630935490131378},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.43833574652671814},{"id":"https://openalex.org/keywords/uniqueness","display_name":"Uniqueness","score":0.4342087209224701},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.4228288233280182},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3649006485939026},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24862328171730042},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09170398116111755}],"concepts":[{"id":"https://openalex.org/C2779623528","wikidata":"https://www.wikidata.org/wiki/Q864377","display_name":"Multiset","level":2,"score":0.9289169311523438},{"id":"https://openalex.org/C153874254","wikidata":"https://www.wikidata.org/wiki/Q115542","display_name":"Canonical correlation","level":2,"score":0.8806806802749634},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6856631636619568},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.6546556353569031},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5913941860198975},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5557432174682617},{"id":"https://openalex.org/C2779226451","wikidata":"https://www.wikidata.org/wiki/Q903809","display_name":"Functional magnetic resonance imaging","level":2,"score":0.4980323314666748},{"id":"https://openalex.org/C35651441","wikidata":"https://www.wikidata.org/wiki/Q625303","display_name":"Independence (probability theory)","level":2,"score":0.4630935490131378},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43833574652671814},{"id":"https://openalex.org/C2777021972","wikidata":"https://www.wikidata.org/wiki/Q22976830","display_name":"Uniqueness","level":2,"score":0.4342087209224701},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.4228288233280182},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3649006485939026},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24862328171730042},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09170398116111755},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi.2015.7163965","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2015.7163965","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5199999809265137}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1495948108","https://openalex.org/W1969008033","https://openalex.org/W1979766935","https://openalex.org/W2058408209","https://openalex.org/W2061191150","https://openalex.org/W2062195890","https://openalex.org/W2063159792","https://openalex.org/W2089449171","https://openalex.org/W2110168541","https://openalex.org/W2113107995","https://openalex.org/W2113506774","https://openalex.org/W2131436045","https://openalex.org/W2163722029","https://openalex.org/W6683945505"],"related_works":["https://openalex.org/W1984946761","https://openalex.org/W2131436045","https://openalex.org/W2762769322","https://openalex.org/W2578144857","https://openalex.org/W1508127764","https://openalex.org/W2320012436","https://openalex.org/W2533762355","https://openalex.org/W1545779705","https://openalex.org/W2792134523","https://openalex.org/W2982056594"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"an":[5,34],"effective":[6],"technique":[7,61],"to":[8,55],"analyze":[9],"task-based":[10],"functional":[11,17],"connectivity":[12,57,99],"across":[13],"multiple":[14],"subjects":[15],"for":[16],"magnetic":[18],"resonance":[19],"imaging":[20],"(fMRI)":[21],"data.":[22],"Instead":[23],"of":[24,28,44,70],"applying":[25],"the":[26],"assumption":[27],"group-independence":[29],"or":[30],"multiset":[31],"correlation":[32,52],"maximization,":[33],"alternative":[35],"approach":[36],"is":[37,76,86],"adopted":[38],"based":[39,67],"on":[40,68],"a":[41,89],"combined":[42],"framework":[43],"sparse":[45],"dictionary":[46],"learning":[47],"(SDL)":[48],"and":[49,64,82],"multi-set":[50],"canonical":[51],"analysis":[53],"(MCCA)":[54],"obtain":[56],"maps.":[58],"The":[59],"proposed":[60],"encapsulates":[62],"commonality":[63],"uniqueness":[65],"solely":[66],"sparsity":[69],"cross":[71],"dataset":[72],"corresponding":[73],"components.":[74],"It":[75],"validated":[77],"using":[78,88],"real":[79],"fMRI":[80],"data":[81],"its":[83,94],"superior":[84],"performance":[85],"illustrated":[87],"simulation":[90],"study,":[91],"which":[92],"shows":[93],"better":[95],"capability":[96],"in":[97],"obtaining":[98],"maps":[100],"that":[101],"are":[102],"more":[103],"specific.":[104]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
