{"id":"https://openalex.org/W4205681664","doi":"https://doi.org/10.1109/bibm52615.2021.9669871","title":"Learning brain effective connectivity networks via controllable variational autoencoder","display_name":"Learning brain effective connectivity networks via controllable variational autoencoder","publication_year":2021,"publication_date":"2021-12-09","ids":{"openalex":"https://openalex.org/W4205681664","doi":"https://doi.org/10.1109/bibm52615.2021.9669871"},"language":"en","primary_location":{"id":"doi:10.1109/bibm52615.2021.9669871","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm52615.2021.9669871","pdf_url":null,"source":{"id":"https://openalex.org/S4363607735","display_name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5015387666","display_name":"Aixiao Zou","orcid":null},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aixiao Zou","raw_affiliation_strings":["Faculty of Information Technology, Beijing Artificial Intelligence Institute, Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Beijing Artificial Intelligence Institute, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252","https://openalex.org/I4210100255"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035117617","display_name":"Junzhong Ji","orcid":"https://orcid.org/0000-0001-6951-741X"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junzhong Ji","raw_affiliation_strings":["Faculty of Information Technology, Beijing Artificial Intelligence Institute, Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Beijing Artificial Intelligence Institute, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252","https://openalex.org/I4210100255"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"284","last_page":"287"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":1.0,"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":1.0,"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":0.9977999925613403,"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/T11184","display_name":"Neonatal and fetal brain pathology","score":0.9854999780654907,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"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/autoencoder","display_name":"Autoencoder","score":0.8841925859451294},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7582626342773438},{"id":"https://openalex.org/keywords/functional-magnetic-resonance-imaging","display_name":"Functional magnetic resonance imaging","score":0.6400359869003296},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6286881566047668},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.5597230195999146},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5362621545791626},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4924560487270355},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.43687260150909424},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42777323722839355},{"id":"https://openalex.org/keywords/neuroimaging","display_name":"Neuroimaging","score":0.42285558581352234},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.41866326332092285},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3513721525669098},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.10113883018493652}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8841925859451294},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7582626342773438},{"id":"https://openalex.org/C2779226451","wikidata":"https://www.wikidata.org/wiki/Q903809","display_name":"Functional magnetic resonance imaging","level":2,"score":0.6400359869003296},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6286881566047668},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.5597230195999146},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5362621545791626},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4924560487270355},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.43687260150909424},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42777323722839355},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.42285558581352234},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.41866326332092285},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3513721525669098},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.10113883018493652},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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:10.1109/bibm52615.2021.9669871","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm52615.2021.9669871","pdf_url":null,"source":{"id":"https://openalex.org/S4363607735","display_name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1495459926","https://openalex.org/W2006240253","https://openalex.org/W2078204079","https://openalex.org/W2118791853","https://openalex.org/W2135404705","https://openalex.org/W2165840723","https://openalex.org/W2756208844","https://openalex.org/W2803807974","https://openalex.org/W2805957063","https://openalex.org/W2912746915","https://openalex.org/W2950101480","https://openalex.org/W2963582879","https://openalex.org/W2983713520","https://openalex.org/W2998537888","https://openalex.org/W3004864201","https://openalex.org/W3128794716","https://openalex.org/W6737512320"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2988134182","https://openalex.org/W2770818364","https://openalex.org/W2786391746","https://openalex.org/W4381430104","https://openalex.org/W2995102745","https://openalex.org/W4226059458","https://openalex.org/W2914559142","https://openalex.org/W1990237101","https://openalex.org/W3196471634"],"abstract_inverted_index":{"Learning":[0],"brain":[1,84,107,128,151,170],"effective":[2,85],"connectivity":[3],"networks":[4],"(ECNs)":[5],"by":[6,142],"means":[7],"of":[8,39,54,106,127],"deep":[9],"learning":[10,65],"methods":[11,31],"from":[12,102],"functional":[13],"magnetic":[14],"resonance":[15],"imaging":[16],"(fMRI)":[17],"data":[18,105,126,134,141],"is":[19,46,135,165],"a":[20,37,61,118,157],"novel":[21,62],"study":[22],"hot":[23],"in":[24,26],"neuroinformatics":[25],"recent":[27],"years.":[28],"However,":[29],"current":[30],"need":[32],"manually":[33],"tune":[34,79],"and":[35,82],"set":[36],"lot":[38],"model":[40,80],"hyper-parameters.":[41],"Once":[42,130],"the":[43,52,89,99,103,113,123,131,162],"parameter":[44],"setting":[45],"unreasonable,":[47],"it":[48,116],"will":[49],"seriously":[50],"restrict":[51],"performance":[53],"algorithms.":[55],"In":[56,87],"this":[57],"paper,":[58],"we":[59],"propose":[60],"method":[63,91],"for":[64],"ECNs":[66],"based":[67,111],"on":[68,112,156],"controllable":[69],"variational":[70],"autoencoder":[71],"(CVAE),":[72],"named":[73],"as":[74],"CVAEEC.":[75],"It":[76],"can":[77,147],"automatically":[78],"parameters":[81],"learn":[83,169],"connectivity.":[86],"detail,":[88],"proposed":[90,163],"first":[92],"adopts":[93],"an":[94,149],"encoder":[95],"network":[96,120],"to":[97,121,138,167,173],"obtain":[98,122],"latent":[100,114],"variables":[101],"fMRI":[104,125,133,140],"regions.":[108,129],"And":[109],"then,":[110],"variables,":[115],"utilizes":[117],"decoder":[119],"generated":[124,132],"highly":[136],"similar":[137],"real":[139,158],"iteratively":[143],"training,":[144],"CVAEEC":[145,164],"algorithm":[146],"output":[148],"optimal":[150],"ECN.":[152],"The":[153],"experimental":[154],"results":[155],"dataset":[159],"show":[160],"that":[161],"able":[166],"better":[168],"ECN":[171],"compared":[172],"some":[174],"state-of-the-art":[175],"methods.":[176]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
