{"id":"https://openalex.org/W2969745688","doi":"https://doi.org/10.23919/eusipco.2019.8902660","title":"fMRI BOLD signal decomposition using a multivariate low-rank model","display_name":"fMRI BOLD signal decomposition using a multivariate low-rank model","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2969745688","doi":"https://doi.org/10.23919/eusipco.2019.8902660","mag":"2969745688"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco.2019.8902660","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2019.8902660","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 27th European Signal Processing Conference (EUSIPCO)","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/A5009767856","display_name":"Hamza Cherkaoui","orcid":"https://orcid.org/0000-0002-3745-4140"},"institutions":[{"id":"https://openalex.org/I4210128565","display_name":"CEA Paris-Saclay","ror":"https://ror.org/03n15ch10","country_code":"FR","type":"government","lineage":["https://openalex.org/I2738703131","https://openalex.org/I277688954","https://openalex.org/I4210128565"]},{"id":"https://openalex.org/I2738703131","display_name":"Commissariat \u00e0 l'\u00c9nergie Atomique et aux \u00c9nergies Alternatives","ror":"https://ror.org/00jjx8s55","country_code":"FR","type":"funder","lineage":["https://openalex.org/I2738703131"]},{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Hamza Cherkaoui","raw_affiliation_strings":["CEA Saclay, Univ. Paris-Saclay, Gif-sur Yvette, France"],"affiliations":[{"raw_affiliation_string":"CEA Saclay, Univ. Paris-Saclay, Gif-sur Yvette, France","institution_ids":["https://openalex.org/I277688954","https://openalex.org/I2738703131","https://openalex.org/I4210128565"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075323166","display_name":"Thomas Moreau","orcid":"https://orcid.org/0000-0002-1523-3419"},"institutions":[{"id":"https://openalex.org/I4210126360","display_name":"Inria Saclay - \u00cele de France","ror":"https://ror.org/0315e5x55","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283","https://openalex.org/I4210126360"]},{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en informatique et en automatique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1326498283"]},{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Thomas Moreau","raw_affiliation_strings":["Parietal Team, INRIA Saclay, Universit\u00e9 Paris-Saclay, Saclay, France"],"affiliations":[{"raw_affiliation_string":"Parietal Team, INRIA Saclay, Universit\u00e9 Paris-Saclay, Saclay, France","institution_ids":["https://openalex.org/I4210126360","https://openalex.org/I1326498283","https://openalex.org/I277688954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064381812","display_name":"Abderrahim Halimi","orcid":"https://orcid.org/0000-0002-8112-5352"},"institutions":[{"id":"https://openalex.org/I32062511","display_name":"Heriot-Watt University","ror":"https://ror.org/04mghma93","country_code":"GB","type":"education","lineage":["https://openalex.org/I32062511"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Abderrahim Halimi","raw_affiliation_strings":["School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK","institution_ids":["https://openalex.org/I32062511"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019588779","display_name":"Philippe Ciuciu","orcid":"https://orcid.org/0000-0001-5374-962X"},"institutions":[{"id":"https://openalex.org/I4210128565","display_name":"CEA Paris-Saclay","ror":"https://ror.org/03n15ch10","country_code":"FR","type":"government","lineage":["https://openalex.org/I2738703131","https://openalex.org/I277688954","https://openalex.org/I4210128565"]},{"id":"https://openalex.org/I2738703131","display_name":"Commissariat \u00e0 l'\u00c9nergie Atomique et aux \u00c9nergies Alternatives","ror":"https://ror.org/00jjx8s55","country_code":"FR","type":"funder","lineage":["https://openalex.org/I2738703131"]},{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Philippe Ciuciu","raw_affiliation_strings":["CEA Saclay, Univ. Paris-Saclay, Gif-sur Yvette, France"],"affiliations":[{"raw_affiliation_string":"CEA Saclay, Univ. Paris-Saclay, Gif-sur Yvette, France","institution_ids":["https://openalex.org/I277688954","https://openalex.org/I2738703131","https://openalex.org/I4210128565"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5009767856"],"corresponding_institution_ids":["https://openalex.org/I2738703131","https://openalex.org/I277688954","https://openalex.org/I4210128565"],"apc_list":null,"apc_paid":null,"fwci":0.4864,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.63053562,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9997000098228455,"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":0.9997000098228455,"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/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9993000030517578,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9977999925613403,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/functional-magnetic-resonance-imaging","display_name":"Functional magnetic resonance imaging","score":0.7142096757888794},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6993771195411682},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6981652975082397},{"id":"https://openalex.org/keywords/voxel","display_name":"Voxel","score":0.6713218092918396},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6553136110305786},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.6149200797080994},{"id":"https://openalex.org/keywords/neural-coding","display_name":"Neural coding","score":0.5127993226051331},{"id":"https://openalex.org/keywords/resting-state-fmri","display_name":"Resting state fMRI","score":0.4619602859020233},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4519892632961273},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2596255838871002}],"concepts":[{"id":"https://openalex.org/C2779226451","wikidata":"https://www.wikidata.org/wiki/Q903809","display_name":"Functional magnetic resonance imaging","level":2,"score":0.7142096757888794},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6993771195411682},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6981652975082397},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.6713218092918396},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6553136110305786},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.6149200797080994},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.5127993226051331},{"id":"https://openalex.org/C66324658","wikidata":"https://www.wikidata.org/wiki/Q7316120","display_name":"Resting state fMRI","level":2,"score":0.4619602859020233},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4519892632961273},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2596255838871002},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/eusipco.2019.8902660","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2019.8902660","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 27th European Signal Processing Conference (EUSIPCO)","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":21,"referenced_works":["https://openalex.org/W1969811075","https://openalex.org/W2023513683","https://openalex.org/W2024729467","https://openalex.org/W2025319485","https://openalex.org/W2047188683","https://openalex.org/W2050717100","https://openalex.org/W2067456724","https://openalex.org/W2077745487","https://openalex.org/W2093366270","https://openalex.org/W2100556411","https://openalex.org/W2105464873","https://openalex.org/W2111609296","https://openalex.org/W2113889563","https://openalex.org/W2117621792","https://openalex.org/W2151590495","https://openalex.org/W2151969869","https://openalex.org/W2180180824","https://openalex.org/W2803751491","https://openalex.org/W2921891170","https://openalex.org/W4295750005","https://openalex.org/W4300043207"],"related_works":["https://openalex.org/W2601707947","https://openalex.org/W2168298321","https://openalex.org/W2387620927","https://openalex.org/W2294986132","https://openalex.org/W2365936003","https://openalex.org/W4401572343","https://openalex.org/W2790620361","https://openalex.org/W1964455563","https://openalex.org/W2319066238","https://openalex.org/W2371524820"],"abstract_inverted_index":{"Standard":[0],"methodologies":[1],"for":[2,126,200],"functional":[3],"Magnetic":[4],"Resonance":[5],"Imaging":[6],"(fMRI)":[7],"data":[8,95,199],"analysis":[9],"decompose":[10],"the":[11,30,34,39,45,116,127,149,155,159,163,216],"observed":[12],"Blood":[13],"Oxygenation":[14],"Level":[15],"Dependent":[16],"(BOLD)":[17],"signals":[18,161],"using":[19,179],"voxel-wise":[20],"linear":[21],"model":[22,91,109],"and":[23,48,119,162,193,227],"perform":[24],"maximum":[25],"likelihood":[26],"estimation":[27,157],"to":[28,77,143,196,205],"get":[29],"parameters":[31],"associated":[32,138,164],"with":[33,139],"regressors.":[35],"In":[36],"task":[37,197],"fMRI,":[38],"latter":[40],"are":[41],"usually":[42],"defined":[43],"from":[44],"experimental":[46],"paradigm":[47],"some":[49],"confounds":[50],"whereas":[51],"in":[52,81,103,106,148,225],"resting-state":[53,68],"acquisitions,":[54,69],"a":[55,82,88,100,120,152,170,206,222],"seed-voxel":[56],"time-course":[57],"may":[58],"be":[59],"used":[60],"as":[61,105,169],"predictor.":[62],"Nowadays,":[63],"most":[64],"fMRI":[65,93,198],"datasets":[66],"offer":[67],"requiring":[70],"multivariate":[71],"approaches":[72],"(e.g.,":[73],"PCA,":[74],"ICA,":[75,107],"etc)":[76],"extract":[78],"meaningful":[79],"information":[80],"data-driven":[83],"manner.":[84],"Here,":[85],"we":[86],"propose":[87],"novel":[89],"low-rank":[90],"of":[92,98,158],"BOLD":[94],"but":[96],"instead":[97],"considering":[99],"dimension":[101],"reduction":[102],"space":[104],"our":[108,211],"relies":[110],"on":[111,191],"convolutional":[112],"sparse":[113],"coding":[114],"between":[115],"hemodynamic":[117],"system":[118],"few":[121],"temporal":[122,141],"atoms":[123],"which":[124],"code":[125],"neural":[128,160,218],"activity":[129],"inducing":[130],"signals.":[131],"A":[132,174],"rank":[133],"-1":[134],"constraint":[135],"is":[136,167,177,188,213],"also":[137],"each":[140],"atom":[142],"spatially":[144],"map":[145],"its":[146],"influence":[147],"brain.":[150],"Within":[151],"variational":[153],"framework,":[154],"joint":[156],"spatial":[165],"maps":[166],"formulated":[168],"nonconvex":[171],"optimization":[172],"problem.":[173],"local":[175],"minimizer":[176],"computed":[178],"an":[180],"efficient":[181],"alternate":[182],"minimization":[183],"algorithm.":[184],"The":[185],"proposed":[186],"approach":[187,208],"first":[189],"validated":[190],"simulations":[192],"then":[194],"applied":[195],"illustration":[201],"purpose.":[202],"Its":[203],"comparison":[204],"state-of-the-art":[207],"suggests":[209],"that":[210],"method":[212],"competitive":[214],"regarding":[215],"uncovered":[217],"fingerprints":[219],"while":[220],"offering":[221],"richer":[223],"decomposition":[224],"time":[226],"space.":[228]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
