{"id":"https://openalex.org/W7160833418","doi":"https://doi.org/10.48550/arxiv.2605.07026","title":"Learning Cross-Atlas Consistent Brain Disorder Representations via Disentangled Multi-Atlas Functional Connectivity Learning","display_name":"Learning Cross-Atlas Consistent Brain Disorder Representations via Disentangled Multi-Atlas Functional Connectivity Learning","publication_year":2026,"publication_date":"2026-05-07","ids":{"openalex":"https://openalex.org/W7160833418","doi":"https://doi.org/10.48550/arxiv.2605.07026"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.07026","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07026","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.07026","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135840423","display_name":"Minheng Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Minheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135850784","display_name":"Chao Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Chao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135897774","display_name":"Jing Zhang","orcid":"https://orcid.org/0009-0001-0147-3315"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135843154","display_name":"Tianming Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Tianming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135858846","display_name":"Dajiang Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Dajiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9908999800682068,"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":0.9908999800682068,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.00559999980032444,"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/T10581","display_name":"Neural dynamics and brain function","score":0.0005000000237487257,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.49459999799728394},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.48570001125335693},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.4738999903202057},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.4478999972343445},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.44589999318122864},{"id":"https://openalex.org/keywords/decorrelation","display_name":"Decorrelation","score":0.43970000743865967},{"id":"https://openalex.org/keywords/functional-connectivity","display_name":"Functional connectivity","score":0.41819998621940613},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.40299999713897705},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.3847000002861023},{"id":"https://openalex.org/keywords/connectome","display_name":"Connectome","score":0.3686000108718872}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5954999923706055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5947999954223633},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.49459999799728394},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.48570001125335693},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.4738999903202057},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45489999651908875},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.4478999972343445},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.44589999318122864},{"id":"https://openalex.org/C177860922","wikidata":"https://www.wikidata.org/wiki/Q788608","display_name":"Decorrelation","level":2,"score":0.43970000743865967},{"id":"https://openalex.org/C3018011982","wikidata":"https://www.wikidata.org/wiki/Q7316120","display_name":"Functional connectivity","level":2,"score":0.41819998621940613},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.40299999713897705},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3847000002861023},{"id":"https://openalex.org/C45715564","wikidata":"https://www.wikidata.org/wiki/Q1292103","display_name":"Connectome","level":3,"score":0.3686000108718872},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.36039999127388},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.35420000553131104},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.3433000147342682},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.3312999904155731},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.33070001006126404},{"id":"https://openalex.org/C152478114","wikidata":"https://www.wikidata.org/wiki/Q660910","display_name":"Neurophysiology","level":2,"score":0.3287999927997589},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.3278000056743622},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3215999901294708},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.304500013589859},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.29649999737739563},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.27900001406669617},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C2780196419","wikidata":"https://www.wikidata.org/wiki/Q1762173","display_name":"Nerve net","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C142853389","wikidata":"https://www.wikidata.org/wiki/Q744778","display_name":"Association (psychology)","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.07026","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07026","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.07026","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07026","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Functional":[0],"connectivity":[1],"(FC)":[2],"derived":[3,93],"from":[4,94],"resting-state":[5],"fMRI":[6],"is":[7],"widely":[8],"used":[9],"to":[10,41,117],"characterize":[11],"large-scale":[12],"brain":[13,30,96],"network":[14],"alterations":[15],"in":[16],"neurological":[17],"and":[18,32,43,114,126,139,148,159,177],"psychiatric":[19],"disorders.":[20],"However,":[21],"FC":[22,91],"construction":[23],"critically":[24],"depends":[25],"on":[26,156],"the":[27,144,152,157,185],"choice":[28],"of":[29,146,188],"atlas,":[31],"different":[33,95],"parcellations":[34],"may":[35],"emphasize":[36],"distinct":[37],"organizational":[38],"features,":[39],"leading":[40],"heterogeneous":[42,196],"sometimes":[44],"inconsistent":[45],"representations.":[46],"Existing":[47],"multi-atlas":[48,174,179],"approaches":[49],"partially":[50],"alleviate":[51],"this":[52],"issue":[53],"but":[54],"often":[55],"fuse":[56],"atlas-derived":[57],"features":[58],"or":[59,167],"predictions":[60],"at":[61],"a":[62,83,101],"relatively":[63],"shallow":[64],"level,":[65],"while":[66],"single-atlas":[67,172],"disentanglement":[68,190],"methods":[69],"do":[70],"not":[71],"explicitly":[72,103],"address":[73],"cross-atlas":[74,119],"heterogeneity.":[75],"We":[76],"propose":[77],"Multi-Atlas":[78],"Disentangled":[79],"Connectivity":[80],"LEarning":[81],"(MADCLE),":[82],"multi-branch":[84],"representation":[85],"learning":[86],"framework":[87],"that":[88,163],"jointly":[89],"encodes":[90],"matrices":[92],"atlases.":[97],"Rather":[98],"than":[99],"introducing":[100],"single":[102],"shared":[104],"latent":[105],"variable":[106],"across":[107],"parcellations,":[108],"MADCLE":[109,164],"learns":[110],"atlas-wise":[111],"disease-related":[112,153],"representations":[113],"encourages":[115],"them":[116],"be":[118],"consistent":[120],"through":[121],"distributional":[122],"alignment.":[123],"Meanwhile,":[124],"covariate-related":[125],"atlas-dependent":[127],"residual":[128],"factors":[129],"are":[130],"modeled":[131],"separately":[132],"using":[133],"covariate":[134],"similarity":[135],"supervision,":[136],"atlas-specific":[137],"reconstruction,":[138],"decorrelation":[140],"constraints,":[141],"thereby":[142],"reducing":[143],"leakage":[145],"non-disease":[147],"parcellation-dependent":[149],"information":[150],"into":[151],"embeddings.":[154],"Experiments":[155],"ADNI":[158],"ADHD-200":[160],"datasets":[161],"suggest":[162],"achieves":[165],"competitive":[166],"improved":[168],"performance":[169],"compared":[170],"with":[171],"baselines,":[173],"GNN/Transformer":[175],"models,":[176],"recent":[178],"consistency":[180],"frameworks.":[181],"These":[182],"results":[183],"support":[184],"potential":[186],"value":[187],"structured":[189],"for":[191],"FC-based":[192],"disorder":[193],"identification":[194],"under":[195],"parcellation":[197],"schemes.":[198]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-12T00:00:00"}
