{"id":"https://openalex.org/W2080141704","doi":"https://doi.org/10.1109/isbi.2013.6556665","title":"FMRI analysis of cocaine addiction using k-support sparsity","display_name":"FMRI analysis of cocaine addiction using k-support sparsity","publication_year":2013,"publication_date":"2013-04-01","ids":{"openalex":"https://openalex.org/W2080141704","doi":"https://doi.org/10.1109/isbi.2013.6556665","mag":"2080141704"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2013.6556665","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2013.6556665","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE 10th International Symposium on Biomedical Imaging","raw_type":"proceedings-article"},"type":"conference-paper","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/A5030248379","display_name":"Katerina Gkirtzou","orcid":"https://orcid.org/0000-0002-4725-3094"},"institutions":[{"id":"https://openalex.org/I216852786","display_name":"\u00c9cole Centrale Paris","ror":"https://ror.org/00v0y5771","country_code":"FR","type":"education","lineage":["https://openalex.org/I216852786"]},{"id":"https://openalex.org/I280199911","display_name":"Bouygues (France)","ror":"https://ror.org/056185y51","country_code":"FR","type":"company","lineage":["https://openalex.org/I280199911"]},{"id":"https://openalex.org/I4210107720","display_name":"CentraleSup\u00e9lec","ror":"https://ror.org/019tcpt25","country_code":"FR","type":"facility","lineage":["https://openalex.org/I277688954","https://openalex.org/I4210107720"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Katerina Gkirtzou","raw_affiliation_strings":["Center for Visual Computing, \u00c9cole Centrale Paris, France","GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content (Laboratoire MAS Ecole Centrale de Paris Grande Voie des Vignes 92295 Chatenay-Malabry Cedex - France)","CVN - Centre de vision num\u00e9rique (CentraleSup\u00e9lec - B\u00e2timent Bouygues - 3 rue Joliot Curie - 91190 GIF-SUR-YVETTE - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Visual Computing, \u00c9cole Centrale Paris, France","institution_ids":["https://openalex.org/I216852786"]},{"raw_affiliation_string":"GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content (Laboratoire MAS Ecole Centrale de Paris Grande Voie des Vignes 92295 Chatenay-Malabry Cedex - France)","institution_ids":["https://openalex.org/I216852786"]},{"raw_affiliation_string":"CVN - Centre de vision num\u00e9rique (CentraleSup\u00e9lec - B\u00e2timent Bouygues - 3 rue Joliot Curie - 91190 GIF-SUR-YVETTE - France)","institution_ids":["https://openalex.org/I280199911","https://openalex.org/I4210107720"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038965118","display_name":"Jean Honorio","orcid":"https://orcid.org/0000-0002-6448-0598"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jean Honorio","raw_affiliation_strings":["Computer Science Departement, Stony Brook University, Stony Brook, NY, USA","Computer Science Department [SUNY] (Stony Brook University, Stony Brook, NY 11794-4400 - United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Departement, Stony Brook University, Stony Brook, NY, USA","institution_ids":["https://openalex.org/I59553526"]},{"raw_affiliation_string":"Computer Science Department [SUNY] (Stony Brook University, Stony Brook, NY 11794-4400 - United States)","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076307452","display_name":"Dimitris Samaras","orcid":"https://orcid.org/0000-0002-1373-0294"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dimitris Samaras","raw_affiliation_strings":["Computer Science Departement, Stony Brook University, Stony Brook, NY, USA","Department of Computer Science [Stonybrook - NY] (Department of Computer Science * Stony Brook University, Stony Brook, NY 11794-4400 - United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Departement, Stony Brook University, Stony Brook, NY, USA","institution_ids":["https://openalex.org/I59553526"]},{"raw_affiliation_string":"Department of Computer Science [Stonybrook - NY] (Department of Computer Science * Stony Brook University, Stony Brook, NY 11794-4400 - United States)","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066625393","display_name":"Rita Z. Goldstein","orcid":"https://orcid.org/0000-0002-1680-229X"},"institutions":[{"id":"https://openalex.org/I98704320","display_name":"Icahn School of Medicine at Mount Sinai","ror":"https://ror.org/04a9tmd77","country_code":"US","type":"education","lineage":["https://openalex.org/I1320796813","https://openalex.org/I98704320"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rita Goldstein","raw_affiliation_strings":["Icahn School of Medicine, Mount Sinai, New York, NY, USA","Laboratory of Molecular Neuropsychiatry (One Gustave L. Levy Place New York, NY 10029-6574 - United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Icahn School of Medicine, Mount Sinai, New York, NY, USA","institution_ids":["https://openalex.org/I98704320"]},{"raw_affiliation_string":"Laboratory of Molecular Neuropsychiatry (One Gustave L. Levy Place New York, NY 10029-6574 - United States)","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077783791","display_name":"Matthew B. Blaschko","orcid":"https://orcid.org/0000-0002-2640-181X"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I142631665","display_name":"\u00c9cole nationale des ponts et chauss\u00e9es","ror":"https://ror.org/02nwvxz07","country_code":"FR","type":"education","lineage":["https://openalex.org/I142631665","https://openalex.org/I4210145102"]},{"id":"https://openalex.org/I216852786","display_name":"\u00c9cole Centrale Paris","ror":"https://ror.org/00v0y5771","country_code":"FR","type":"education","lineage":["https://openalex.org/I216852786"]},{"id":"https://openalex.org/I280199911","display_name":"Bouygues (France)","ror":"https://ror.org/056185y51","country_code":"FR","type":"company","lineage":["https://openalex.org/I280199911"]},{"id":"https://openalex.org/I4210107720","display_name":"CentraleSup\u00e9lec","ror":"https://ror.org/019tcpt25","country_code":"FR","type":"facility","lineage":["https://openalex.org/I277688954","https://openalex.org/I4210107720"]},{"id":"https://openalex.org/I4210152518","display_name":"Laboratoire d'Informatique Gaspard-Monge","ror":"https://ror.org/04t50yk91","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I142631665","https://openalex.org/I4210145102","https://openalex.org/I4210152518","https://openalex.org/I4210154111","https://openalex.org/I4210159245"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Matthew B. Blaschko","raw_affiliation_strings":["LIGM (UMR CNRS), \u00c9cole des Ponts ParisTech, Universit\u00e9 Paris-Est, Paris, France","GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content (Laboratoire MAS Ecole Centrale de Paris Grande Voie des Vignes 92295 Chatenay-Malabry Cedex - France)","CVN - Centre de vision num\u00e9rique (CentraleSup\u00e9lec - B\u00e2timent Bouygues - 3 rue Joliot Curie - 91190 GIF-SUR-YVETTE - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LIGM (UMR CNRS), \u00c9cole des Ponts ParisTech, Universit\u00e9 Paris-Est, Paris, France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I142631665","https://openalex.org/I4210152518"]},{"raw_affiliation_string":"GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content (Laboratoire MAS Ecole Centrale de Paris Grande Voie des Vignes 92295 Chatenay-Malabry Cedex - France)","institution_ids":["https://openalex.org/I216852786"]},{"raw_affiliation_string":"CVN - Centre de vision num\u00e9rique (CentraleSup\u00e9lec - B\u00e2timent Bouygues - 3 rue Joliot Curie - 91190 GIF-SUR-YVETTE - France)","institution_ids":["https://openalex.org/I280199911","https://openalex.org/I4210107720"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"58","issue":null,"first_page":"1078","last_page":"1081"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9965000152587891,"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":0.9965000152587891,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9835000038146973,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/elastic-net-regularization","display_name":"Elastic net regularization","score":0.9382944107055664},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.9350709915161133},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.6333230137825012},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5952991247177124},{"id":"https://openalex.org/keywords/lasso","display_name":"Lasso (programming language)","score":0.5917432904243469},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5590970516204834},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48328641057014465},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.4813600182533264},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.42576029896736145},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4078254997730255},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29871606826782227},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.14992594718933105}],"concepts":[{"id":"https://openalex.org/C203868755","wikidata":"https://www.wikidata.org/wiki/Q5353562","display_name":"Elastic net regularization","level":3,"score":0.9382944107055664},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9350709915161133},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.6333230137825012},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5952991247177124},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.5917432904243469},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5590970516204834},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48328641057014465},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.4813600182533264},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.42576029896736145},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4078254997730255},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29871606826782227},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.14992594718933105},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"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/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi.2013.6556665","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2013.6556665","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE 10th International Symposium on Biomedical Imaging","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.800000011920929,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1800516388","https://openalex.org/W1972853750","https://openalex.org/W2014848045","https://openalex.org/W2030134080","https://openalex.org/W2041050058","https://openalex.org/W2067150211","https://openalex.org/W2090392676","https://openalex.org/W2098950991","https://openalex.org/W2106867389","https://openalex.org/W2107159574","https://openalex.org/W2115631892","https://openalex.org/W2119122547","https://openalex.org/W2122825543","https://openalex.org/W2126817288","https://openalex.org/W2134869028","https://openalex.org/W2135046866","https://openalex.org/W2154930578","https://openalex.org/W2166403493","https://openalex.org/W6677598287","https://openalex.org/W6680156768","https://openalex.org/W7008237156"],"related_works":["https://openalex.org/W2468272465","https://openalex.org/W2429564919","https://openalex.org/W757986597","https://openalex.org/W4212910148","https://openalex.org/W4213314600","https://openalex.org/W4210804200","https://openalex.org/W4399551893","https://openalex.org/W2954774635","https://openalex.org/W4392841950","https://openalex.org/W3123944750"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,60,90],"explore":[4],"various":[5],"sparse":[6,136],"regularization":[7,26,85],"techniques":[8],"for":[9,169],"analyzing":[10],"fMRI":[11,40,166],"data,":[12],"such":[13],"as":[14,53,55],"LASSO,":[15],"elastic":[16,99,124],"net":[17,100,127],"and":[18,59,78,128,143,172],"the":[19,31,72,84,96,102,123,132,147,158,162],"recently":[20],"introduced":[21],"k-support":[22,103,129,148,163],"norm.":[23],"Employing":[24],"sparsity":[25,111],"allow":[27],"us":[28],"to":[29,83,110],"handle":[30],"curse":[32],"of":[33,49,134,161],"dimensionality,":[34],"a":[35,107],"problem":[36,133],"commonly":[37],"found":[38],"in":[39,139,165],"analysis.":[41],"We":[42,87],"test":[43],"these":[44],"methods":[45],"on":[46],"real":[47],"data":[48],"both":[50,140,168],"healthy":[51],"subjects":[52],"well":[54],"cocaine":[56],"addicted":[57],"ones":[58],"show":[61],"that":[62,89,119],"although":[63],"LASSO":[64,97],"has":[65],"good":[66,141],"prediction,":[67],"it":[68],"lacks":[69],"interpretability":[70],"since":[71],"resulting":[73,138],"model":[74],"is":[75,106,120],"too":[76],"sparse,":[77],"results":[79,156],"are":[80],"highly":[81],"sensitive":[82],"parameter.":[86],"find":[88],"can":[91],"improve":[92],"prediction":[93,142,152,170],"performance":[94,171],"over":[95],"using":[98],"or":[101],"norm,":[104],"which":[105],"convex":[108],"relaxation":[109],"with":[112],"an":[113],"\u2113":[114],"<sub":[115],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[116],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sub>":[117],"penalty":[118],"tighter":[121],"than":[122],"net.":[125],"Elastic":[126],"norm":[130,149,164],"overcome":[131],"overly":[135],"solutions,":[137,145],"interpretable":[144],"while":[146],"gave":[150],"better":[151],"performance.":[153],"Our":[154],"experimental":[155],"support":[157],"general":[159],"applicability":[160],"analysis,":[167],"interpretability.":[173]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":3}],"updated_date":"2026-07-26T07:53:14.480251","created_date":"2025-10-10T00:00:00"}